hypothesis for air pollution experiment

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• Environ Health Perspect
• v.110(1); 2002 Jan

Air pollution and daily mortality: a hypothesis concerning the role of impaired homeostasis.

We propose a hypothesis to explain the association between daily fluctuations in ambient air pollution, especially airborne particles, and death rates that can be tested in an experimental model. The association between airborne particulates and mortality has been observed internationally across cities with differing sources of pollution, climates, and demographies and has involved chiefly individuals with advanced chronic illnesses and the elderly. As these individuals lose the capacity to maintain stable, optimal internal environments (i.e., as their homeostatic capacity declines), they become increasingly vulnerable to external stress. To model homeostatic capacity for predicting this vulnerability, a variety of regulated physiologic variables may be monitored prospectively. They include the maintenance of deep body temperature and heart rate, as well as the circadian oscillations around these set-points. Examples are provided of the disruptive changes shown by these variables in inbred mice as the animals approach death. We consider briefly the implications that the hypothesis may hold for several epidemiologic issues, including the degree of prematurity of the deaths, the unlikelihood of a threshold effect, and the role that coarse, noncombustive particles may play in the association.

The Full Text of this article is available as a PDF (719K).

Selected References

These references are in PubMed. This may not be the complete list of references from this article.

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Air pollution and vegetation: hypothesis, field exposure, and experiment.

Published online by Cambridge University Press:  05 December 2011

Unravelling the subtle effects of air pollution on vegetation requires adherence to the experimental method for testing hypotheses. Three experimental approaches are described. Field release of pollutants causes minimal disturbance of other aspects of the environment but is difficult to control and to operate continuously. Closed chambers , such as glasshouses, are furthest removed from field conditions but many aspects of their environments can be controlled. There is scope for the more sophisticated use of computer controlled glasshouses to investigate responses of stands of crop plants and natural/semi natural communities. Open-top chambers (OTCs) are a popular research tool, but results from major studies such as the U.S. National Crop Loss Assessment Network are of uncertain general value. Incursion of air into the tops of OTCs creates vertical pollution gradients. Evaporation, and the stomatal control of transpiration in OTCs may be very different from that in the field. Uptake of pollutant gases in OTCs may also differ from that in the field, directly because of differences in air movement, and/or indirectly through differences in the distribution of temperatures and moisture.

The development and design of a U.K. research programme on red spruce is used to illustrate (i) the need to develop hypotheses from a wide range of observations, (ii) the advantages of using a range of experimental approaches and (iii) the requirement to synthesise results before reaching general conclusions.

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• M. H. Unsworth (a1)
• DOI: https://doi.org/10.1017/S0269727000005327

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Teaching AP® Science

Resources By Kristi Schertz

Air Pollution Lab- Airborne Particulates for Distance Learning

One of the best and easy-to-implement labs in my class is an air pollution lab. Traditionally, my students are in person as we use petri dishes, but this year, I came up with an at-home lab for distance learning. This lab can be used in many different courses and I use it in unit 7 for AP® Environmental Science. Click for a student version of the at-home lab . The original in-person lab can be found on this post.

At this point in my curriculum, my students have performed a controlled a experiment about soil salinization and have designed a noise pollution lab in Unit 5 so they do not need much scaffolding. This lab gives them further practice with experimental design.

Experimental design lab is essential for students to do a few times in the year, because the AP® Exam WILL have experimental design questions in the multiple choice section and on FRQ #1. It is AP Science Practice # 4: Scientific Experiments .

Materials for the Airborne Particulates At-Home Lab

• 3 index cards OR pieces of paper
• Vaseline, Chapstick, lip gloss or something else that clear and sticky
• Ruler or PDF of ruler
• Ziploc bag with toothpicks, or a sealed food container
• Magnifier on a Smartphone (Best option is a free app with magnifying glass with light, but you can also use the built-in magnifier on a phone along with a separate light source)

Day one of the air pollution lab takes about 45-60 minutes. Student lab groups brainstorm and come up with a question to test, a hypothesis, and design. They must get approval from me before making their cards.  My students have already done an experimental design lab so this process is fairly quick at this point. If this is the first experimental design lab of the year, expect this to take longer and for students to need more revision.

This lab is challenging with the constants. They can never really isolate all the variables and because of this, they will get flawed data. This is really important!!   Analyzing the weaknesses in their lab help them identify flawed experiments later on in life and on the AP® Exam. I aim to develop scientifically literature citizens.

I give students some ideas such as comparing indoor vs. outdoor particulates, front yard vs. back yard or the number of pets. Some students come up with very creative ideas outside of these suggestions.

If rain is in the forecast, make sure they don’t set out the cards in the rain (or sprinklers). Also, they need to make sure they all set out the cards on the same day for the same amount of time, because weather can influence.

After approvals, students make their cards. Students need to put one card in a sealed bag or food container as the control.

Here is a YouTube video I made showing students how to make their cards.

Day 2 of the Airborne Particulates Lab

Day 2 of the air pollution lab is several days later . With this lab, it can take a week to get enough particulates that students can see with their phone magnifiers instead of a stereoscope.

Students can download a free app “magnifying glass+ light” or they can use the built-in magnifier on their phones. If they use the built-in ones, they will need another source of bright light. Below is a screenshot of a the particulates using the app.

I provide a spreadsheet template for students to use if they wish, but they will not turn it in as I am grading their graphs. I also provide sample data and graphs as reference and a YouTube Link for how to make graphs using this google sheets template. (Click on links in this paragraph for these resources)

My students make and present posters for this air pollution lab and in distance learning, they make a google slide. Click for a template of the google slide presentation that my students fill in per group . It really helps them discuss and analyze the results. Why their hypothesis was correct or not AND more importantly, why this lab was flawed. They can never fully control all the variables and I want them to see that other factors may have influenced their results. This is the best part of the lab–learning to identify flawed experiments.

My poster template is inspired by Argument-Based Inquiry, but I have added more sections and clearer instructions.

You can also have students write a formal lab report individual or as a group as assessment as well.

Click for a poster rubric.

* AP ®  is a trademark registered and/or owned by the College Board which was not involved in the production of, and does not endorse this site.

Kristi Schertz

You might also like, group data analysis.

Human Population 5E Lab Using Cemetery Data Part II

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Science project, counting air particulate matter.

The purpose of my project was to find out whether a rural area or an urban area produces more particulate air pollution. The project also tried to identify which type of habitat within these areas (forest, field, and road) produced more particulate air pollution. To determine this I put microscope slides that were coated with a thin layer of Vaseline out in each of these areas and habitats for 24-hour periods. The particulate matter that settled on the slides stuck to the Vaseline and I was able to count the number of particles under a dissecting microscope.

The data show that the rural areas sampled received the most particulate air pollution when compared to the urban sites. This was true for all the habitats combined for the rural and urban area and also for each of the habitat types. The data also show that in the rural areas, the forest received the most particulate matter, the field the second most amount, and the road the least. The data for the urban habitats were different. As expected, the road site received the most particulate matter, but the forest received the second highest amount, and the field the least. The urban road habitat received the most particulate matter because of the automobile and truck traffic. But the reason the urban sites in general received the least amount of particulate matter is because most of the pollution produced by automotive vehicles are chemical pollutants and not large enough to be collected or counted with a dissecting microscope. The reason the rural habitats had such a high level of particulate matter is because at the times the samples were run, the corn and soybean harvest was in progress. There is considerable dust, dirt, and plant particle debris that are blown into the air by the combines in the harvest process.

Is There More Air Particulate Matter in an Urban or Rural Area?

I chose the problem "Is there more Air Particulate Matter in an Urban or Rural Area" because I was interested in finding out whether air pollution is different in an urban and rural area. Also, I wanted to find out whether there is a difference in particulate air pollution depending, on the type of community or habitat. I also want to find out if the amount of particulate air pollution in an area is constant over time, or are there other factors that affect it, like weather or human activity.

Air pollution is known to be a health hazard. I was interested in doing an experiment to find out some of the patterns of air pollution.

• Is there a difference between air pollution in urban areas as compared to more rural areas?
• In a setting (urban versus rural), is there a difference in air pollution within different communities?
• Is the quantity of air pollution the same over time or does it vary with temperature, wind, or human activity?

I used a measure of particulate air pollution to answer some of these questions.

Background Information

Pollutants are any materials, such as gas, particles, or chemicals that are released into the environment. The class of pollutants that are released into the atmosphere is known as air pollutants and is called air pollution. Air pollution can be in gaseous or particulate forms. Major sources of air pollutants are from the burning of fuels and wastes, factory emissions, automobile exhaust, and cigarette smoke. There are natural sources of air pollution as well. For example, the dust and salt blown from the ocean or the "haze" produced by plants in our large forests are natural forms of air pollution.

The chief forms of chemical pollutants in air pollution are carbon dioxide (CO2), carbon monoxide (CO), volatile-organic compounds (VOC), nitrogen oxide (NOX), and sulfur oxide (SOX). These chemicals can mix together to form other chemical compounds that are also pollutants. These pollutants can have major effects on humans. Such as, a headache, itchy-red eyes, dizziness, tiredness, trouble breathing, colds, the flu, lung problems, even an increased death rate.

Another form of air pollution comes from particulate matter. It is called Particulate Air Pollution. This type of air pollution refers to microscopic, air-born particles, that are suspended in the atmosphere. Particulate matter is 10 microns or less in size. This is the same as one half of the width of an average human hair. Most sources of particulate air pollution come from organic sources. But most organic materials are not harmful. Some of the sources of particulate pollution in the atmosphere are from tobacco smoke, wood smoke, burning of coal, logging, earth moving (construction, road building), and diesel-burning vehicles. Even lawn mowers produce small particles when grass and leaves are chopped up and blown out from the mower. Particulate Air Pollution is a human health hazard because it can travel into your lungs and cause a variety of respiratory problems. Children and senior citizens are affected the most. Particulate matter can be cause an increase in emergency room visits, hospitalization, days off of school and work, heart and lung problems, and an increase death rate.

In addition to human health problems, both chemical and particulate air pollution are prime factors involved with the warming of the earth by the greenhouse effect and the depletion of the earth’s protective ozone layer. There are ways we can tell how bad the air pollution is around us.

The way we do this is with the "Air Quality Index"

http://www.lungusa.org/air/air_index.html

I believe that there will be more air particulate matter in the urban areas than in the rural areas. From the background reading I learned that most of the sources of particulate matter come from urban areas. Automobiles and industry are the most common sources of particulate air pollution and there are more of both in urban areas as compared to rural areas.

I also hypothesize that within each area, the three different community types will have different quantities of particulate pollution. The road will have more than the fields, which will have more than the forests (roads > fields > forests). I believe this because there is more activity that produces particulate pollution near a road. In the fields there is air circulation, which will bring in particles but it is not near the sources of pollution. The forest will have the best air quality because the air is still and the tree’s leaves block particles.

• 6 or more slides (3in-1in)
• Permanent marker
• 3 sites in an urban area
• Forest - Millstream Park
• Field – Millstream Park * Road – Railroad Ave.
• 3 sites in a rural area
• Forest – Back yard, Deerfield
• Field – Back yard, Deerfield
• Road – Coon Box Rd.
• Dissecting microscope
• Choose three similar sites in both an urban and rural area: along a major road, grass covered open area, and forest.
• Mark two equal areas (1.5 cm x 1.0 cm = 1.5 cm2) on the back of each slide with a permanent marker. Each 1.5 cm2 area constitutes a sample.
• Label one section of the slide A the other B. Make sure that all labeling is on the back of the slide, otherwise the sample areas and the labeling may be mistakenly rubbed off.
• Give each slide a number 1-6 and have a key for the area and place where the sample will be taken in. For example: #1-urban field, #2-urban road, etc.
• On the opposite side you marked on, apply a thin coating of Vaseline. Rub any excess off the slide to make the Vaseline coating even. (When particulate matter lands on the Vaseline it will stick to the slide.)
• In the three different areas lay the slide on a flat surface (fallen tree, block of wood, etc.) and in an open area.
• Leave the slides out for 24 hours to collect particulate matter.
• Record weather conditions for the 24-hour period; high and low temperature, wind velocity.
• Record any peculiar activity that may have occurred in the vicinity of a sample during the 24-hour period.
• Collect the samples and place them in a covered container so they are not contaminated with additional particulate material as you leave the site.
• Carefully, without smearing the Vaseline, take each slide and put them under a dissecting microscope.
• Under the microscope count the particulate matter in each of the 1.5 cm2 sample sections and record your observations.
• Repeat this procedure a minimum of three times per site.

My data show that there is more air pollution particles in the rural areas as compared to the urban areas (Table 1). When all of the samples in all of the communities were averaged together, the total particles for the rural areas averaged 153.40 particles/1.5 cm2 and the urban average was 134.07 particles/1.5 cm2 (Fig. 1). Looking at all the data from each one of the 3 trial taken, in every case the rural areas had a higher number of particles than when compared to the urban areas (Fig. 2). In the three trials the particles in the rural area were 445.5, 459.5, and 475.5 particles/1.5 cm2 and the three urban trials were 348.0, 424.5, and 453.0 particles/1.5 cm2 (Table 1). These findings are completely opposite my hypothesis where I thought that there would be less particulate pollution in the rural areas as compared to the urban areas.

I think that there are several reasons why my experiment produced these results. First, the samples were taken during the period of mid October to mid November. This was the finishing of the corn harvest and during the soybean harvest. Harvesting crops with a combine produces lots of dust and chopped up plants that are blown in the air. I think these particles were what landed on my slides. The urban areas are far enough away from the harvesting that the particles were not blown there. If I had done this test during the summer when there was no harvesting or when farmers are not working their fields, I think I would have different results. I also think that the particles that are produced in urban areas may be too small to be seen with a dissecting microscope. Particles of soot from car and truck exhaust are very small as compared to dust, soil particles, and plant parts that are blown in the air from harvesting.

One other hypothesis I had was that there would be a difference in the particulate air pollution in different types of habitats. My hypothesis was that there would be more particles in near the road and the least amount would be in the woods. I thought the field sites would be somewhere in between. The data for the rural areas (Fig. 3) show that the most particles were collected from the forest site and the least from the road site. This is completely opposite my hypothesis. I this is because the forest slows wind movement and allows particles to settle better than along the road or in a field.

Unlike the rural area, the urban area did agree with my hypothesis that the road would have the most particulate pollution (Fig. 4) when compared to other habitats. But the urban field did show the lowest amount of particles.

In conclusion, my project was to find out, "Is there more air particulate matter in an urban or a rural area". For my project I chose three sites in an urban area and three sites in rural area. Then I placed slides smeared with a thin layer of Vaseline in each of these sites to capture particulate air pollution. I repeated the experiment three times and averaged the results. Based on my date there was more air particulate matter in the rural area as compared to the urban area. This was because the time of the year I sampled was during the corn and soybean harvest and that produced a lot of material. I do not think that this level of particulate matter is constant throughout the year but does occur when farmers are active in their fields. Also, the soot particles produced by automobiles and trucks in the urban area may have been too small to see and count.

The data that I recorded was opposite my hypothesis in one case but not another. I thought there would be more particulate pollution along the road as compared to the field or the forest. In the rural area my hypothesis was wrong. But in the urban area it was correct. I think that forests slow the movement of air. In the rural areas where there was crop harvesting, the particulate matter settled out. But in the urban area where there was no plant particles to settle out in the forest, the road had the greatest amount of particulate air pollution.

Bibliography

• Williams L. Ramsey, Lucretia A. Gabriel, James F. McGuirk, Clifford R. Phillips, Frank M. Watenpaugh, 1982, Holt Science textbook, Holt, Rinehart &Winton Publishing, pp. 472-477.
• American Lung Association, The Air Quality Index - Fact Sheet.
• www.Bigchalk.com/environmental protection
• Boy Scouts of America, 1998. Environmental Science Merit Badge Handbook, Irving, TX, pp. 96.
• Environmental Protection Agency (EPA) (www.epa.gov) Air Quality Index, Fact Sheet.
• EPA, (www.epa.gov), Particulate Matter. * EPA, (www.epa.gov), Air Now.
• Miller, Tyler, 1988. Environmental Science, 2nd edition. Wadsworth Belmont CA, pp. 318-346.
• Oregon Department of the Environment, (www.deq.state.or.us), Air Quality Index.

See http://www.qacps.k12.md.us/cms/sci/ercesair.HTM#Data

See http://www.qacps.k12.md.us/cms/sci/ercesair.HTM#Graph

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Air Pollution

Introduction: (initial observation).

Air pollution is the contamination of the air by noxious gases and minute particles of solid and liquid matter (particulates) in concentrations that endanger health. In addition to many economical and agricultural losses, air pollution is the main cause of many diseases and deaths every year. Excessive growth rate of air pollution is a major concern for many countries and scientists from all over the world are studying on causes, prevention methods and cleanup of the air pollution.

This project is an opportunity to follow the foot steps of other scientists and learn about the air pollution causes and cleanups.

This project guide contains information that you need in order to start your project. If you have any questions or need more support about this project, click on the “ Ask Question ” button on the top of this page to send me a message.

If you are new in doing science project, click on “ How to Start ” in the main page. There you will find helpful links that describe different types of science projects, scientific method, variables, hypothesis, graph, abstract and all other general basics that you need to know.  

Project advisor

Information Gathering:

Find out about air pollution. Read books, magazines or ask professionals who might know in order to learn about the causes of air pollution and methods of prevention and cleanup. Keep track of where you got your information from.

For basic general information,  encyclopedia  is a good start.

Air Pollution Control

To show how air pollution is controlled.

Grade level

6th, 7th & 8th grades

Essential Elements

(Science) 1 (A) Properly demonstrate the use of laboratory equipment; 2 (A) Observe physical and chemical properties of matter; 5 (A) Measure physical and chemical properties of matter.

At the end of the lesson the student will be able to distinguish between an electrostatic precipitator and a wet scrubber and the principles behind the operation of these control techniques.

When any product is made by industry, waste may be produced that can pollute the air. Wet scrubbers and electrostatic precipitators are two devices used to clean up the air waste stream before it enters the atmosphere.

Air contaminants are emitted into the atmosphere as particulates, aerosols, vapors, or gases. The most common methods of eliminating or reducing pollutants to an acceptable level are destroying the pollutant by thermal or catalytic combustion, changing the pollutant to a less toxic form, or collecting the pollution by use of equipment to prevent its escape into the atmosphere. Pollutant recovery may be accomplished by the use of one or more of the following:

Baghouses  – Dry particulates are trapped on filters made of cloth, paper or similar materials. Particles are shaken or blown from the filters down into a collection hopper. Baghouses are used to control air pollutants from steel mills, foundries, and other industrial furnaces and can collect more than 98 percent of the particulates. Cyclones  – Dust-laden gas is whirled very rapidly inside a collector shaped like a cylinder. The swirling motion creates centrifugal forces causing the particles to be thrown against the walls of the cylinder and to drop into a hopper. Cyclones are used for controlling pollutants from cotton gins, rock crushers, and many other industrial processes and can remove up to 95 percent of solid pollutants. Electrostatic precipitators  – By use of static electricity, they attract particles in much the same way that static electricity in clothing picks up small bits of dust and lint. Electrostatic precipitators, 98 to 99 percent effective, are used instead of baghouses when the particles are suspended in very hot gases, such as in emissions from power plants, steel and paper mills, smelters, and cement plants. Wet scrubbers  – Particulates, vapors, and gases are controlled by passing the gas stream through a liquid solution. Scrubbers are used on coal burning power plants, asphalt/concrete plants, and a variety of other facilities that emit sulfur dioxides, hydrogen sulfides, and other gases with a high water solubility. Wet scrubbers are often used for corrosive, acidic, or basic gas streams. ( Note that recovery control devices include adsorption and condenser techniques as well.)

• Which type of air cleaner would be the best for removing particles?
• Which type of air cleaner would be the best for removing waste gases?
• Does a wet scrubber clean up all of the pollutants?
• What problems are produced by having too many pollutants in the air we breathe?
• If industry is just part of the problem, what can we do to control the amount of air pollution that we cause?

Question/ Purpose:

What do you want to find out? Write a statement that describes what you want to do. Use your observations and questions to write the statement.

The purpose of this project is to demonstrate at least one of the air filtration methods. Construct a filter and show that it actually does collect or filter some pollutants.

Possible questions are:

Which filtration method is best for particle pollution? Which area has the highest amount of invisible pollutants? What are the causes of air pollution and how can it be prevented? (After identifying the cause of pollution, we can simply stop it by switching to other methods that do not cause pollution. For example if we identify fossil fuels such as coal and oil as a source of pollution, we can try using solar energy, hydroelectric energy or wind energy.) How effective is any system of air filtration?

Identify Variables:

When you think you know what variables may be involved, think about ways to change one at a time. If you change more than one at a time, you will not know what variable is causing your observation. Sometimes variables are linked and work together to cause something. At first, try to choose variables that you think act independently of each other. For question 1, variables are:

The independent variable (also known as manipulated variable) is the filtration method.

The dependent variable (also known as responding variable) is the amount of pollutants they filter.

Constants are the type of pollutants and filtration time.

For question 2, variables are:

The independent variable (also known as manipulated variable) is the location.

The dependent variable (also known as responding variable) is the pollution rank.

Constants are the experiment method, time and supplies.

Hypothesis:

Based on your gathered information, make an educated guess about what types of things affect the system you are working with. Identifying variables is necessary before you can make a hypothesis.

Sample Hypothesis:

My hypothesis is that by passing polluted air through water we can filter pollutants and produce clean air. This hypothesis is based on my observation of air freshness after a heavy rain.

Experiment Design:

Design an experiment to test each hypothesis. Make a step-by-step list of what you will do to answer each question. This list is called an experimental procedure. For an experiment to give answers you can trust, it must have a “control.” A control is an additional experimental trial or run. It is a separate experiment, done exactly like the others. The only difference is that no experimental variables are changed. A control is a neutral “reference point” for comparison that allows you to see what changing a variable does by comparing it to not changing anything. Dependable controls are sometimes very hard to develop. They can be the hardest part of a project. Without a control you cannot be sure that changing the variable causes your observations. A series of experiments that includes a control is called a “controlled experiment.”

Experiment 1:

(Visible and Invisible pollutants)

Which area has the highest amount of invisible pollutants?

The atmosphere is almost completely made up of invisible gaseous substances. Most major air pollutants are also invisible, although large amounts of them concentrated in areas such as cities can be see as smog. One often visible air pollutant is particulate matter, especially when the surfaces of buildings and other structures have been exposed to it for long periods of time or when it is present in large amounts. Particulate matter is made up of tiny particles of solid matter and/or droplets of liquid. Natural sources include volcanic ash, pollen, and dust blown by the wind. Coal and oil burned by power plants and industries and diesel fuel burned by many vehicles are the chief sources of man-made particulate pollutants, but not all important sources are large scale. The use of wood in fireplaces and wood-burning stoves also produces significant amounts of particulate matter in localized areas, although the total amounts are much smaller than those from vehicles, power plants, and industries.

In this experiment we will test for visible and invisible pollutants in the air and try to tell the difference between visible and invisible air pollution.

chart paper measuring cups small glass jars large glass jars petroleum jelly 3 bean plants approximately the same size tap water vinegar vinegar-water mixture in 1 to 3 ratio pH paper or indicator

Visible Pollutants test

Smear petroleum jelly on each small jar. Carefully place each small jar inside a large jar. Decide on several places around the school or home where you think visible pollutants will occur. Make predictions about which area will have more visible pollutants and why. Record predictions in journal. Place jars in test areas for several days. Check the jars daily. Record observations in journal. Collect jars for comparison. Observe and rank the jars from the one with the most visible pollutants to the one with the least. Assign each jar a number. Discuss why certain areas have more visible pollutants than others. Mark a map showing the ranking of areas from the lowest dust to the highest dust.

Invisible Pollutants test

Sets up a bean plant garden with three containers, each container having one bean plant. Determine and compare the pH of the three solutions and predict how the plants will be affected by each solution. Record pH and predictions in journal. Plants will be watered every day with 1/8 to 1/4 cup of a solution: one plant with tap water, one plant with straight vinegar, and one plant with the vinegar-water mixture. Procedure is recorded in journal. Observe plants daily. Record in journal what happens to each plant. Sketches may be part of the observations. Compare plants and discuss observations at the end of a day, week, two weeks, or until plants die. Using the observations, write a conclusion for this experiment. Record in journal. Invisible pollutants are like acid rain. Use the result of your experiment to conclude how does acid rain affect the plants.

Research the history of acid rain. Include information on the causes of acid rain, when we first became aware of the problem, what problems have been caused by acid rain, what measures have been taken to

combat acid rain. Has the situation improved? Post a chart for the causes of visible pollutants and what can be done to prevent them.

Experiment 2: Make a electrostatic precipitator Particles (called particulate matter) can be captured before they enter the atmosphere by an electrostatic precipitator. In this experiment we use a plastic tube and black pepper to see how particles are attracted to the sides of the tube much like the pollutants are attracted in large industrial electrostatic precipitators.

Materials, Equipment, and Preparation plastic tube (fluorescent light tube) wire coat hanger plastic grocery bag electric blow dryer punch holes, black pepper or rice crispies Picture on the right shows an industrial model of electrostatic precipitator.

The electrostatic precipitator works on the principle of a static electric charge attracting particles where they are removed.

A 2-foot plastic tube in which fluorescent lights are stored can be used to simulate an electrostatic precipitator. The plastic tube can be charged by running a coat hanger with a plastic grocery bag attached to it.

(The plastic bag as it moves through the tube strips the negatively charged electrons from the inside of the tube making the overall net charge positive. Anything that has a negative charge will be attracted to the tube because opposites attract.)

Hold the tube over some punch holes, black pepper, or rice crispies. Hold an electric hair dryer so the air stream blows across the top of the tube. The air mass creates a low pressure area at the top and the greater air pressure at the bottom pushes the punch holes up the tube. (This is called Bernoulli’s Principal)

***The Results*** If the tube is charged, the punch holes will stick to the sides. This activity can be used to study static electricity. If the tube is not charged, the holes will shoot out in a spray. This activity can be used to study Bernoulli’s principle.

Experiment 3: How to Make a Wet Scrubber

Warning: This experiment requires proper equipment and expert adult supervision. Please skip this experiment without proper equipment and supervision.

The wet scrubber is one of the most common pollution control devices used by industry. It operates on a very simple principle: a polluted gas stream is brought into contact with a liquid so that the pollutants can be absorbed. In this experiment we will try to build a wet scrubber. (See diagram A)

Materials Paper towels 12-cm piece of glass Three 2.5-cm pieces of glass tubing Three 55-ml flasks Two glass impingers (glass tubing drawn at one end to give it a smaller diameter so as to let out smaller bubbles) Heat source (burner or hot plate) Three 2-hole rubber stoppers (of a size to fit the mouths of the flasks) Two 30-cm pieces of rubber tubing Ring stand apparatus Vacuum source Procedure Write your answers on a separate sheet. Set up the apparatus as shown in attached figure . Put a paper towel in a 55-ml flask and place this above the burner. Using a 2-hole stopper that makes an air-tight seal with the flask, insert a 12-cm section of glass tubing through one of the holes. The tubing should reach to approximately 1.2-cm from the bottom of the flask. Insert a 2.5-cm piece of glass tubing into the other hole of the stopper. Connect a 30-cm piece of rubber tubing to the 2.5-cm piece of glass tubing, making sure an air-tight seal exists. Fill a second 500-ml flask approximately 3/4 full of water. Using a second double-hole stopper, put a 2.5-cm piece of glass tubing into one of the holes, and insert the glass impinger into the other. Construct a third flask like the second. Connect the rubber tubing and heat the first flask (combustion chamber) until smoke appears. Put a vacuum on the third flask to draw a stream of smoke through the second flask (the wet scrubber). If smoke collects in the second flask above the water, a second scrubber can be added. Ask the students if particles are the only pollutants produced by industry. Discuss how a wet scrubber collects not only particulate matter but also captures waste gases. Demonstrate how the water scrubber works. Discuss that the white plume you see coming from a smokestack may really be steam coming from a water scrubber. After observing the wet scrubber, answer the following questions: Why does the water in the wet-scrubber change color? Why does the wet-scrubber have an impinger (in other words, why is it important for small bubbles to be formed)? What does the scrubber filter out of the air? Not filter out? Suggest ways to dispose of the pollutants that are now trapped in the water.

Materials and Equipment:

List of material can be extracted from the experiment section.

Results of Experiment (Observation):

Experiments are often done in series. A series of experiments can be done by changing one variable a different amount each time. A series of experiments is made up of separate experimental “runs.” During each run you make a measurement of how much the variable affected the system under study. For each run, a different amount of change in the variable is used. This produces a different amount of response in the system. You measure this response, or record data, in a table for this purpose. This is considered “raw data” since it has not been processed or interpreted yet. When raw data gets processed mathematically, for example, it becomes results.

Calculations:

Description

Summery of Results:

Summarize what happened. This can be in the form of a table of processed numerical data, or graphs. It could also be a written statement of what occurred during experiments.

It is from calculations using recorded data that tables and graphs are made. Studying tables and graphs, we can see trends that tell us how different variables cause our observations. Based on these trends, we can draw conclusions about the system under study. These conclusions help us confirm or deny our original hypothesis. Often, mathematical equations can be made from graphs. These equations allow us to predict how a change will affect the system without the need to do additional experiments. Advanced levels of experimental science rely heavily on graphical and mathematical analysis of data. At this level, science becomes even more interesting and powerful.

Conclusion:

Using the trends in your experimental data and your experimental observations, try to answer your original questions. Is your hypothesis correct? Now is the time to pull together what happened, and assess the experiments you did.

Related Questions & Answers:

What you have learned may allow you to answer other questions. Many questions are related. Several new questions may have occurred to you while doing experiments. You may now be able to understand or verify things that you discovered when gathering information for the project. Questions lead to more questions, which lead to additional hypothesis that need to be tested.

Possible Errors:

If you did not observe anything different than what happened with your control, the variable you changed may not affect the system you are investigating. If you did not observe a consistent, reproducible trend in your series of experimental runs there may be experimental errors affecting your results. The first thing to check is how you are making your measurements. Is the measurement method questionable or unreliable? Maybe you are reading a scale incorrectly, or maybe the measuring instrument is working erratically.

If you determine that experimental errors are influencing your results, carefully rethink the design of your experiments. Review each step of the procedure to find sources of potential errors. If possible, have a scientist review the procedure with you. Sometimes the designer of an experiment can miss the obvious.

References:

List of References

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Air Pollution Experiment

I recently did a mini unit with my students on urban ecology. We were learning about the effects of urbanization on ecosystems, and pollution and urban heat islands came up in our discussions. (You can read my blog post about urban heat islands here ). Here in Phoenix it is relatively easy to see how polluted our air is, all you have to do is drive up a hill and you will see the layer of haze that sits over our city of 1.6 million people. We discussed the health effects of air pollution and I wanted my students to have a visual of what they were breathing in. You can buy fancy (and expensive) sensors that will give you data readings of all the particles in the air, but I found an easy way for students to see the particulate matter floating around.  ​ You will need:

• Glass slides (gridded slides are ideal) 
• Cover slips
• Compound microscopes
• Petroleum jelly or double sided tape
• Cotton swabs
• Optional lab write up can be found here

Students got to choose where they wanted to leave their vasaline-covered slide for 24 hours. I had some students leave the slides in the classroom and others left their slides outside. (Tip: I had students set them in a petri dish and label them with their initials so we could track them down easier the next day. Also, if students choose to leave them outside, find a location on your school campus where they won’t get disturbed). In the next 24 hours, any particulate matter floating around will land on the slide and stick to the petroleum jelly. If you want easier cleanup, you can also try putting a piece of double sided tape on the slide instead. 

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8 Student Experiments to Measure Air Quality

As pollution is becoming more rampant worldwide, educators are deeply encouraged to teach students about the science of pollution and what they can do to care for the environment. From the release of greenhouse gases, worsening climate change, and burning of fossil fuels, the rate at which pollutants are produced is unprecedented. Hence, we have suggested 8 experimental ideas that teachers can use to actively involve their students to measure air quality. These ideas range from qualitative to quantitative methods and promote active discussion on which type of method would prove the most effective in producing reliable data. By measuring air quality, students can make sense of the different standards of air quality and recognize when it becomes appropriate to mitigate their effects. Without further ado, let’s jump into it!

PM 2.5 Meter

Particulate Matter (PM) 2.5 refers to particles or compounds which spans less than 2.5 micrometers in diameter. They are fine particles that are commonly produced from factories, vehicle exhaust, burning of crops or volcanic eruptions. Long-term exposure to high concentrations of these particles can put people at risk of developing respiratory illnesses, inflammation, trigger asthma and can even lead to lung cancer.

There are many types of PM 2.5 meters in the market, with some of them capable of measuring other types of pollutants as well. PM 2.5 is most commonly measured in units of µg/m3 (micrograms per meter cubed), or through AQI measurements (meter below ).

Depending on the meter you purchase, it may display either one of the units of measurement. However, keep in mind that the two measurements are not directly proportional to each other as measurements in µg/m3 are representative of the actual amount of PM in the area whereas AQI measurements are subjective to the air quality standards that have been internationally agreed upon for your particular region. This difference in proportionality is represented in the following graph :

Having a corresponding AQI level alongside PM2.5 measurements can help students measure the precise amount of PM2.5 in a certain area and use them to determine the quality of the air. Students could measure air quality in different areas, comparing the results obtained and discuss the probable causes and sources of PM from the location. It is also a great way to measure quantitative values that are useful for writing scientific reports while reinforcing the numbers with a qualitative interpretation of the air quality.

You could perform these measurements with your students for a variety of experiments. For example, by comparing the PM measurements from outdoor areas to indoors, students would become aware of the differences in air quality and discuss the possible outdoor/indoor pollutants that may cause one to have higher PM levels than the other. Another great experiment would be to compare the PM levels of rooms that use purifiers and those that do not. This would demonstrate the effectiveness of air purifiers to students and how they are beneficial to maintaining their general wellbeing.

The following graph compares the different levels of PM 2.5 and US AQI PM 2.5 measurements provided by sarta.innovations2019.org :

Blue Sky Test

The color of the sky is a reliable qualitative method to measure air quality. The color can change due to airborne particles that reflect and refract light. For example, a blue sky would indicate little to no air pollution whereas bright red ones are a result of heavy pollution.

The University of Southern California developed an algorithm through its mobile sensing project to measure the air quality by analyzing pictures of the sky taken from the Android app that they created. The pictures that would be taken would take into account the user’s location, orientation, time taken and transfer the data collected into their server. It would calibrate the image and compare it with their own model of the sky.

Although their app isn’t officially listed in the Play Store, all you have to do is click this link from your Android mobile device and click ‘Download Android App’ on from their website. If your phone is preventing the download, click here to find out more about installing APK files on your phone.

Mountain Visibility Test

Similar to the blue sky test, checking the visibility of mountains, or a large construction that can be seen from a long distance is also a qualitative indicator for air pollution. When we see places that are more polluted, we easily recognize the thick haze and dust that clearly obscures the view. But if we live in a polluted area, a clear view of mountains or other constructions may seem foreign in comparison as demonstrated from the following pictures provided by the US National Park Service :

This method measures visibility as an indicator of air pollution. A great idea is to get in touch with schools from different areas to see if they would like to collaborate to gather picture samples of their view. This way, teachers could show their students what mountains or distant constructions from various places look like. This can prompt a discussion about why some areas are more visible than others while explaining how air pollution impacts the view.

Students could also compare the images with public available AQI data from the region and see if there is a direct correlation between the AQI and visibility. Using the previous AQI Index table, students would be able to understand the different standards of air quality and associate it to qualitative observations on their surrounding environment. Furthermore, you could perform this experiment using AQI websites such as AirVisual or aqicn.org to identify the AQI values of different locations worldwide and compare it with images of landmarks in a particular country from which students could effectively assess the visibility.

This is a simple and easy qualitative analysis that can be performed anywhere in the world. However, it is advisable to make observations in the morning when there is the least fog and other factors impacting visibility .

Sticky Tape Method

Although the most dangerous particles are smaller in size, it is still a good indicator of air pollution to also measure the amount of larger particles such as dust, soot, dirt, smoke that can be potentially seen.

The sticky tape method is very simple, all you have to do is cut a small piece of transparent sticky tape and attach it to the bark of a tree or the surface of a building. Leave it for 10 seconds to let any PM on the surface stick onto the tape, peel the tape off and stick it onto a piece of paper. Students should be advised to label the time and location at which they took the sample.

Students could perform experiments by either collecting tape samples in the same location over different periods of time or taking samples in different locations at a certain period of time depending on their chosen independent and dependent variables. They can make qualitative observations of how PM levels change in different times and locations. This can be expanded by discussing the possible reasons as to why some areas or times have more PM in the air than others.

Lichen Observation & App

Sulfur dioxide (SO2) is a gas with a pungent scent which is known to be harmful towards our health. It is mostly generated from the burning of fossil fuels from industrial processes such as the generation of electricity from burning coal. It reacts to evaporated moisture in the air to produce several acidic compounds such as sulfuric acid, which can cause acid rain when dissolved in rainwater, leading to the acidification of forests.

Nitrogen can also be an overlooked pollutant as it is a common constituent in fertilizers and organic waste from households and sewage. When they have washed away into water bodies, it increases the acidity of the water, causing numerous wildlife deaths and disrupting the ecosystem. Like sulfur dioxide, it also causes acid rain when neutral nitrogen particles react with lightning in the air and mix with rainwater.

Lichen is an effective bio-indicator of sulfur and nitrogen pollutants. If lichen is a naturally occurring substance in your area, it will not be present if they are in the air and there would be green algae in its place. Many more species can act as a bioindicator for particular pollutants depending on vegetation that are sensitive or tolerant to them. A massive study was conducted using lichens to measure the air quality throughout the UK by the OPAL Air Survey .

The study conducted modeled the relationship between lichens as a bioindicator, nitrogenous pollutants, and their climate. Furthermore, the data was easily collected by everyday citizens throughout the UK and can be performed as school experiments as well. The map of the UK on the left demonstrated the amount of nitrogen dioxide (NO2) around the country while the one on the right referred to NHx radicals such as ammonia (NH3) and ammonium (NH4), which can cause ammonia pollution. The following image is their result:

The UK Centre for Ecology & Hydrology developed the Lichen Web-App , which provides guidelines on how to identify what type of lichen is suitable for testing, how to perform chemical tests on them and a comprehensive list of different species that are sensitive or tolerant towards nitrogen. It also enables you to track and record any trunks and branches that have lichens on them. They also created a measurement system called Nitrogen Air Quality Index (NAQI) to accurately associate the different levels of nitrogen to indicate their corresponding level of air quality.

Students could emulate this study on a much smaller scale and explore their environment for lichen or other similar species. This would also make them aware of how vegetation is often sensitive towards pollution.

Palmes Passive Diffusion Tubes

Nitrogen can exist in many forms, one of them being nitrogen dioxide (NO2). Nitrogen dioxide is a gaseous pollutant produced from the burning of fossil fuels such as those in power plants and vehicle exhausts. It undergoes a process in which neutral nitrogen (N2) and oxygen (O2) particles react in high temperatures to produce nitrous oxides (NOx) including NO2, all of which can inflict respiratory conditions such as inflammation, coughing, irritations, etc. This is clearly demonstrated from the image on the right which was performed in an experiment from the University of Edinburgh.

Passive diffusion tubes are an effective long-term method to measure nitrogen dioxide. These small plastic tubes contain a mesh disc made of steel covered with a chemical called triethanolamine (TEA). If nitrogen dioxide is present and passes through the mesh, it would react with TEA and change the color and chemical composition. Diffusion tubes can measure the change in nitrogen dioxide levels over many months inside classrooms or outside your school based on how much TEA is left in the tube.

Ozone Testing Experiments

Ozone (O3) is a gas that is popularly known as a gaseous layer in the stratosphere which protects the earth from harmful UV radiation from the sun. However, ones at the troposphere are mainly the result of the chemical reactions between nitrous oxides (NOx), volatile organic compounds (VOCs) and the sunlight. At high concentrations, they can cause chest pains, coughs, throat irritations and are especially harmful to those suffering from respiratory conditions such as asthma.

We can test for the presence of ozone in two different ways:

Ozone badges are very simple and can be made into different forms. All of them rely on a change in color when high concentrations of ozone are present. The badges as seen from the image are commercially produced indicators that are commonly used by workers who are required to operate in areas with elevated ozone concentrations.

For a more advanced chemical experiment, you could also perform the Schoenbein experiment. Students would require cornstarch and potassium iodide to make indicator strips that would react with ozone if present in the air, evidently turning blue or purple. According to the resulting Schoenbein number from the color scale below, we can determine the amount of ozone present in parts per billion (ppb) as seen from the following from the graph.

It is important to perform this experiment in days with low humidity (the lines from the graph represent how the Schoenbein numbers vary based on the different percentages of humidity in the air). Under these circumstances, ozone would be more likely to break apart into atmospheric oxygen. This experiment also yields the best results in the ozone season, which occurs during heated temperatures throughout the summer and in areas with high vehicle activity.

While this method is relatively safe, it is advised to perform this under the supervision of Chemistry teachers who can provide them with the chemicals and laboratory equipment needed.

Surface Wipes

Surface wipes are similar to the sticky tape method, which simply involves wiping a cotton bud on a surface to observe how much PM was released in a particular time or area. Students can compare the cotton buds that were wiped on the surfaces that are more exposed to the ones less exposed to pollutants, such as on the opposite sides of a handrail or bench. The following video is a lighthearted and entertaining experiment performed by a YouTuber from Sydney to observe the city’s air quality, which has dramatically worsened as of late due to the Australian bushfires:

Teachers and students are encouraged to be creative, improvise and innovate experiments similar to this. That way, educators could create a stimulating and critical learning environment for students to teach them about scientific research methods.

As a teacher or parent, you can choose from a myriad of creative options to teach your child how to measure air quality. Depending on their style of learning and personal preference, you can weigh the benefits of performing qualitative or quantitative methods to help them understand the state of the environment. By performing diverse experiments, they would be able to understand how different collection methods result in corresponding data types. After experimenting with multiple methods, they can then determine which type would be the most suitable to fulfill the research’s purpose. We hope that these experiments would be able to pique their curiosity and encourage them to make meaningful discussions about the health effects and environmental impacts of air pollution!

by Carisa A. Feb 16, 2020

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Methodology of Pollution Ecology: Problems and Perspectives

• First Online: 01 January 2009

Cite this chapter

• Mikhail V. Kozlov 4 ,
• Elena L. Zvereva 4 &
• Vitali E. Zverev 4  

Part of the book series: Environmental Pollution ((EPOL,volume 15))

535 Accesses

Charles J. Krebs (1989) started his famous book Ecological Methodology with the following statement: ‘Ecologists collect data and, as in other fields in biology, the data they collect are to be used for testing hypotheses’. This is true, but where do the new hypotheses originate?

The roots of many theories, including the Newton’s law of universal gravitation and Darwin’s evolutionary theory, emerge from systematic or occasional observations. Accumulation of data on rainfall acidity, measurements of which for a long time were driven by scientific curiosity, in the 1950s and 1960s allowed scientists to determine the origin of acidity and recognise the damaging effects of acid rain. The importance of observational studies (sometimes termed mensurative experiments) still remains high, especially for environmental sciences, which often face novel problems associated with the rapid development of our civilisation.

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Kozlov, M.V., Zvereva, E.L., Zverev, V.E. (2009). Methodology of Pollution Ecology: Problems and Perspectives. In: Impacts of Point Polluters on Terrestrial Biota. Environmental Pollution, vol 15. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2467-1_8

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Graphic of a person walking with an air sensor through a door and graphical text that reads Indoor v. Outdoor.

Free Resources > Indoor-v-Outdoor

Air Quality Experiment: Indoor V. Outdoor

You can design a simple experiment to compare indoor and outdoor air quality by using your Kids Making Sense PM sensor. Begin by measuring air pollution inside of your house, then walk outside, and then back inside. What did you find? Is the air quality better or worse indoors? Can you explain your results?

We tried the experiment in two different locations exposed to wildfire smoke, with surprising results.

We turned on the sensor and measured the particulate matter while standing inside by the front door. Then we opened the front door and walked outside (A) where it was quite smoky. We then came back inside (B) and closed the door.

Experiment 1 showed much higher pollution levels outdoors.

In Experiment 2, the PM concentrations inside were slightly elevated, but not very high. However, when we walked outside (C), PM concentrations decreased. That was not what we expected! We then walked back inside (D) and confirmed that the air outside was actually slightly cleaner than the air inside the home.

As with any scientific experiment, the value is in understanding why we got these results. Some indoor activities that may have caused higher PM levels indoors include cooking and dusting. Another possibility is that polluted air was trapped inside, while winds outside shifted and blew the smoke away.

Experiment 2 showed slightly lower pollution levels outdoors.

In both cases, we could improve our indoor air quality by using an air filter. These two examples highlight that the same experiment can often result in different outcomes - the value is in understanding why!

What will your students discover when they conduct their own experiments?

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Science Experiments for Children - Air Pollution

After a lovely few weeks of sunshine and warm weather, the summer holidays are coming to an end. Now is a great time to get your children motivated for the year ahead with some fun science experiments!

Dr Emily Shuckburgh OBE FRMetS - Director at Cambridge Zero, has made a video of science experiments for school children with a focus on air pollution.  During the experiments children will learn all about lichen, they will get to see how air quality is measured and how we can analyse the data. They will also see how traffic levels can be monitored and how we can teach computers to recognise cars, buses and pedestrians.

Here are some websites you might want to visit for more information:

Lichen: http://www.apis.ac.uk/lichen-app/main

Air quality measurement: https://www.cam.ac.uk/research/impact...

Air quality data: https://www.airqualityengland.co.uk

Teaching computers to do things (“machine learning”): https://machinelearningforkids.co.uk/

If you find out anything interesting in your own studies, you can contact Cambridge Zero at [email protected]   @CambridgeZero    #LockdownScience .

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Did you know that rainfall radar can be hoodwinked by swarms of flying ants? Over several days each summer, we witness “Flying Ant Day” when ants take to the air to mate and disperse.

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Activity: Make Your Own Air Pollution Catcher

Breathing air is vital to our existence, but have you ever thought you might not be breathing purely clean air? This simple experiment will help you determine the amount of foreign particles in the air in a specific area and give you an idea of how “dirty” your air is.

Required Materials

• White paper plates, index cards, or cardstock
• Petroleum jelly (e.g., Vaseline)
• String or yarn
• Hole puncher ( optional )
• Magnifying glass or microscope
• Permanent black marker
• Disposable glove ( optional )
• Ballpoint pen
• Journel or notebook

Estimated Experiment Time

Step-By-Step Procedure

• Find an area in which you can hang the air pollution catcher. You can do this in your home if you’d like to find out how clean the air in your home is, or you can hang one outside in your yard or another area. It also helps to try placing one in a busier area than the other.

Important Note

Adult assistance/supervision is highly recommended when cutting and punching holes into the paper plate or cardstock paper, as well as hanging the pollution catchers in high places so they are not disturbed.

• After 3-7 days, retrieve your pollution catchers.

You will most likely find some amount of particles stuck to the pollution catcher.

• Are there a lot of particles or just a few?
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ORIGINAL RESEARCH article

The impact of the digital economy on environmental pollution: a perspective on collaborative governance between government and public.

• 1 College of Finance and Statistics, Hunan University, Changsha, China
• 2 The Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
• 3 School of Mathematics and Statistics, Hunan Normal University, Changsha, China

The rapid development of the digital economy is driving transformative changes in a multifaceted collaborative environmental governance system. From the perspective of collaborative governance between government and the public, this study employs double fixed-effects models, spatial econometric models, and instrumental variables methods to empirically explore how the digital economy influences environmental pollution, using panel data from 30 provinces in China spanning 2011 to 2022. The results demonstrate that the digital economy significantly lowers environmental pollution. The primary mechanism is through the government’s environmental governance behaviors, which are positively moderated by public environmental concerns, enhancing effectiveness. Additionally, the digital economy induces a spatial spillover effect on environmental pollution. This promotion of collaborative management between the government and the public is poised to become a pivotal direction in future environmental governance.

1 Introduction

As one of the world’s largest energy consumers and carbon emitters, improving environmental pollution in China holds significant importance for the global environment. Yet, China’s environmental challenges are extremely severe. To tackle this, it’s crucial to actively embrace the new development concept, focus on building and enhancing an ecological civilization system, and unwaveringly pursue a green, circular, and low-carbon economic growth path.

The digital economy, which involves economic activities created by connecting individuals, organizations, and information systems via digital technology, is rapidly evolving ( Carlsson, 2004 ; Sturgeon, 2021 ; Zhen et al., 2021 ; Zou and Deng, 2022 ). The influence of the digital economy on environmental pollution is a key focus among scholars. The digital economy reduces environmental pollution by fostering technological innovation and refining industrial structures ( Zhang et al., 2023 ). It also enhances the integration of the real economy with industry using production factors such as knowledge, information, and IT, playing a vital role in boosting production efficiency ( Pan et al., 2022 ), reducing carbon emissions ( Xie et al., 2024 ), and advancing green transformations ( Liu and Zhao, 2024 ). Technologies like artificial intelligence, big data, and industrial robots are reshaping industrial chains ( Moyer and Hughes, 2012 )and enhancing green total factor productivity ( Chen et al., 2023 ; Wang et al., 2024 ), thus driving high-quality development ( Wang et al., 2024 ). From this perspective, studying the impact of the digital economy on environmental pollution and the underlying mechanisms behind it is of significant importance for achieving green and sustainable development.

However, the synergistic mechanisms between government environmental governance and social public participation have not received adequate attention, presenting an opportunity to expand this area of research. The use of big data platforms and Internet technology can enhance the informatization of government environmental oversight, the scientific basis of environmental protection decisions, and deepen social participation ( Wei and Zhang, 2023 ). These advancements improve the government’s capacity to manage the environment and further reduce environmental pollution levels. Notably, a diverse and shared environmental governance model is emerging in China, where the government leads and public participation supports. Public involvement acts as an informal environmental regulation ( Tan and Eguavoen, 2017 ), compelling local governments to focus on environmental protection and increase invest-ments in environmental pollution control ( Ge et al., 2021 ), which contributes to environmental improvement ( Wang et al., 2023 ).

This study investigates how the development of digital economy impacts regional environmental pollution governance through collaborative efforts between the government and the public. The findings indicate that the digital economy significantly reduces environmental pollution levels. This reduction prompts local governments to increase investments in environmental pollution control, enhance their focus on environmental governance, and enforce environmental protection penalties, thereby improving regional environmental quality. These conclusions remain robust after various analyses, including the instrumental variable method and substituting explanatory variables. Public environmental concern also positively moderates the relationship between the digital economy and environmental pollution, indicating that the digital economy enhances environmental governance effectiveness through increased government and public interaction and participation.

The potential marginal contribution of this paper is twofold: Firstly, we use the co-management by multiple environmental stakeholders as the entry point, positioning the digital economy, governmental environmental governance, public environmental concern, and environmental pollution prevention within the same analytical framework to systematically assess the governance effects of the digital economy on environmental pollution. This encourages us to consider the synergistic effects of government environmental governance and social public participation, offering a fresh perspective for deeply understanding how the digital economy contributes to green development. Secondly, this paper delves into the specific transmission mechanisms through the interaction between local governments and the public, aiming to explore the long-term mechanisms of environmental governance facilitated by collaborative efforts between the government and the public.

The structure of the paper is as follows: Section 1 introduces the study. Section 2 reviews relevant literature and presents theoretical analyses. Section 3 outlines the research design, including variable selection, data sources, and model construction. Section 4 analyzes empirical results, covering baseline regression outcomes, impact mechanisms, moderating effects, and tests for spatial effects. Section 5 conducts robustness tests, utilizing approaches like the instrumental variables method, substitution of explanatory variables, and heterogeneity analysis. Finally, Section 6 offers conclusions and policy recommendations.

2 Theoretical mechanism and research hypothesis

2.1 the development of digital economy can reduce environmental pollution.

The rapid advancement and widespread adoption of digital information technologies—such as the Internet, big data, and artificial intelligence—not only inject new momentum into economic development but also facilitate the restructuring of the environmental governance system encompassing government, businesses, and society. This restructuring supports the economy’s transition to green and low-carbon operations. Specifically, within enterprises, the innovative breakthroughs in digital technology serve as a key driver for eco-friendly economic practices. As primary agents of pollution control, businesses utilize digital technologies to gather information and consolidate resources, enabling informed production decisions and enhancing operational efficiency ( Zhang Rongwu et al., 2022 ). Moreover, the digital economy enhances knowledge dissemination efficiency in electronic equipment, communication networks, and information processing, encouraging enterprises to adopt green production models. This undoubtedly fosters technological innovation and industrial upgrading, ultimately contributing to both pollution reduction and green development ( Xu et al., 2023 ).

In terms of governmental environmental governance, digital technology facilitates the development and application of ecological and environmental data, effectively collecting, integrating, and sharing critical information like pollution levels and environmental carrying capacity. This data supports dynamic assessments and supervision of governmental environmental efforts, improving pollution perception and early warning capabilities ( Fang et al., 2024 ), and enhancing the precision and effectiveness of environmental supervision. This provides a robust data foundation for crafting environmental policies and refining the ecological regulation framework, thereby elevating the government’s role in environmental management ( Shin and Choi, 2015 ).

Regarding public supervision and participation, the digital economy simplifies ac-cess to environmental information and raises public environmental awareness by enhancing public service platforms and fostering information exchange between the government and the community. The public can engage in environmental oversight through avenues such as social media, ensuring adherence to environmental regulations and governance policies, and supervising pollution activities and enforcement. This transformation of environmental consciousness into action facilitates collaborative governance of environmental pollution by both government and the public ( Yang et al., 2020 ).

In conclusion, the development of digital economy impacts environmental pollution by promoting green production in enterprises, refining governmental environmental regulatory frameworks, and enhancing social and public oversight. Based on these insights, this paper proposes the following research hypothesis:

Hypothesis 1:. The development of digital economy will reduce environmental pollutant emissions, i.e., digital economic development contributes to effective environmental pollution management.

2.2 Government and Public synergy mechanism

2.2.1 government environmental governance mechanism.

Reducing environmental pollution is a systematic project requiring the participation and synergy of multiple actors, including the government, enterprises, and the public. From the perspective of interactive synergy between the government and the public, analyzing the impact of the digital economy on the environmental governance behaviors of local governments can deepen our understanding of China’s environmental pollution governance model.

Firstly, the digital economy enables the government to efficiently collect, integrate, and share environmental data, scientifically assess government environmental governance performance, and enhance the accuracy and effectiveness of environmental supervision, thus boosting the government’s regulatory capacity ( Zhao Shuliang et al., 2023 ). Additionally, the government can use digital technology to expand communication channels for knowledge diffusion, support environmental regulation, and improve policy formulation and implementation, which also increases government transparency ( Peng et al., 2023 ), and improves environmental governance ( Ahlers and Shen, 2018 ).

Secondly, investment in environmental pollution management reflects the commitment and effort of local governments in environmental governance. The digital economy drives local governments to increase their investments in environmental governance through digitized knowledge, information, technology, and other production factors, thereby elevating the level of ecological and environmental governance ( Su et al., 2018 ; Zhu and Li, 2020 ).

Thirdly, the digital economy aids the development and utilization of environmental data and information, reducing information asymmetry between various government departments, businesses, and the public. This breaks down data barriers, forms a comprehensive ecological and environmental data system, and improves the transparency of local government environmental protection ( Ahlers and Shen, 2018 ). The digital economy also enhances government environmental supervision and law enforcement capabilities, enabling the government to impose penalties on non-compliant businesses ( Wu et al., 2024 ), strengthen the investigation and handling of environmental violations and penalties ( Li Mingxian et al., 2023 ; Liu et al., 2024 ), and deter environmental non-compliance by businesses, which in turn reduces environmental pollution emissions.

Based on the above analysis, the following hypothesis is proposed:

Hypothesis 2:. The digital economy affects environmental pollution governance through the pathways of government environmental governance attention, environmental pollution governance investment, and environmental protection administrative enforcement efforts.

2.2.2 Regulatory effect of Public environmental concern

Social public participation, supplementing environmental regulation, supervises and influences local government environmental governance behaviors. With the rapid advancement of digital technology, the public can share social resources and create public service platforms using the Internet and big data. This platform model enables the public to easily access environmental information, express their opinions, and voice their dissatisfaction with pollution issues and demand for environmental quality improvement ( Tan and Eguavoen, 2017 ). The theory of government responsiveness suggests that government environmental governance behaviors are influenced by and respond to regional public opinion, aligning environmental policies with public preferences ( Arantes, 2023 ). In response to public environmental demands, the government reduces Environmental pollution by increasing attention to environmental governance, boosting investment in pollution control, and imposing administrative penalties for environmental protection ( Sun et al., 2023 ; Liu et al., 2024 ).

On the other hand, under the Chinese environmental governance model, public satisfaction with environmental governance is a crucial metric for assessing local government performance. The digital economy promotes public environmental demands, participation, and supervision, compelling higher-level governments to motivate and oversee lower-level government environmental policies and behaviors, which assists in reducing local environmental pollution ( Niu et al., 2024 ). As the primary responder, local governments attend to public environmental demands and actively respond through their governance practices, adjusting their environmental policy preferences, which undoubtedly enhances the environmental governance performance of local governments. The digital economy influences local government environmental policy adjustments and governance behaviors by elevating public environmental concerns and transforming public demands into active participation in environmental protection activities ( Arantes, 2023 ).

The above logical mechanism is summarized as follows: The digital economy contributes to the interaction between the government and the public, influences public environmental awareness and behavior, and guides the public to pay attention to and participate in the process of environmental governance, thus fostering a governance system that is scientific in decision-making, refined in supervision, and convenient in service. By influencing local government environmental governance behaviors, public environmental demands compel local governments to enhance their focus on environmental governance, increase investments in environmental pollution control and enforcement, and ultimately aid in regional environmental pollution control.

As a result, the following hypothesis is derived:

Hypothesis 3:. Public environmental concern positively moderates the relationship between the digital economy and environmental pollution, enhancing the impact of digital economy on government environmental regulation.

2.3 Spatial spillover effects

The spatial spillover effect of the digital economy and environmental pollution is a crucial prerequisite for spatial measurement research. The spatial spillover effect of the digital economy has been extensively studied ( Li Guangqin et al., 2023 ; Hou et al., 2023 ; Xu, 2024 ), with scholars exploring its impacts on rural revitalization ( Li Guangqin et al., 2023 ) and industrial green innovation ( Li Mingxian et al., 2023 ).

Conversely, the spatial spillover effect of environmental factors has also garnered significant attention ( Liu et al., 2020 ; Zhang Maomao et al., 2022 ; Zhao Feng et al., 2023 ). This includes research on water pollution ( Liu, et al., 2020 ), the interplay between urbanization and environmental pollution ( Zhao Feng et al., 2023 ), the relationship between industrial agglomeration and environmental pollution ( Zhang Maomao et al., 2022 ), and the mismatch of land resources contributing to environmental issues ( Wan and Shi, 2022 ). Given that both the digital economy and environmental pollution exert influences on surrounding areas, it is conceivable that the digital economy might also impact environmental pollution in neighboring regions through spatial spillovers.

Thus, the following hypothesis is proposed:

Hypothesis 4:. The digital economy will exert spatial spillover effects on environmental pollution.

3 Research design

3.1 variable selection, 3.1.1 explained variable.

Environmental pollution level (Pol). The entropy weighting method is used to calculate a comprehensive environmental pollution index for regional industrial wastewater, sulfur dioxide emissions, and solid waste emissions as a measure of the level of environmental pollution in each region.

3.1.2 Main explanatory variable

The explanatory variable in this paper is the digital economy (Dtf). Currently, defining the connotation of the digital economy is challenging, with many scholars understanding it as the sum of economic activities based on modern information technology ( Carlsson, 2004 ; Sturgeon, 2021 ; Zhen et al., 2021 ; Zou and Deng, 2022 ). In this paper, we build on the work of Wang et al. (2021) to construct a digital economy development index using the entropy weight method. We select indicators from four dimensions: digital infrastructure, digital industrialization, industrial digitization, and digital innovation capability. The specific indicators are presented in Table 1 .

Table 1 . Comprehensive Evaluation Indicator System for the development of digital economy.

3.1.3 Mechanistic variables

This study identifies three core mechanistic variables to dissect the behavioral patterns of local governments in environmental governance. First, the degree of pre-existing attention to environmental governance (en) is quantified by analyzing the frequency of environmental and haze-related terms in the annual work reports of provincial governments. Secondly, the financial commitment to environmental pollution control (ei) is gauged by the total provincial investment in this area. Finally, the post-action intensity of enforcing environmental regulations (ez) is measured through the tally of environmental administrative penalty cases. These variables are designed to provide a comprehensive evaluation of the government’s dedication and efficacy in environmental protection.

3.1.4 Moderating variable

The moderating variable, public environmental concern (pf), is represented by the Baidu haze search index. This choice is primarily due to Baidu’s status as the largest Chinese search engine, which offers extensive coverage and high data availability, allowing for detailed regional analysis based on search frequency and trends. Haze, as an environmental issue, tends to register a higher level of public awareness compared to other issues like environmental pollution, making it an ideal measure of environmental concern.

3.1.5 Control variables

In a bid to thoroughly examine the digital economy’s influence on environmental pollution, this study introduces various control variables: fiscal freedom (ff), gauged by the ratio of fiscal revenue to fiscal expenditures; financial development level (fin), defined by the urban financial employment per 10,000 people; infrastructure (inf), assessed through the ratio of highway kilometers to developed area; medical care level (sin), measured by the number of practicing assistant physicians per 10,000 people; science and technology investment (tec), represented by the ratio of industrial enterprises’ R&D expenditures to regional GDP; education level (edu), based on the average higher education enrollment per 10,000 population; old-age burden (old), using the elderly dependency ratio; and parenting burden (chi), determined by the child dependency ratio.

3.2 Model Setting

Based on the results of the Hausman test (test value of 39.472, p-value of 0), the fixed effect model is deemed appropriate. Given that the data are panel data, and drawing on the methodology of Zhang et al. (2023) , a double fixed-effect model is employed to analyze the impact of the digital economy on environmental pollution. The specific model (1) is presented as follows.

In Eq. 1 , Poli,t represents the level of environmental pollution in province i during period t; Dtfi,t denotes the level of the digital economy in province i during the same period; the vector Zi,t includes a series of control variables for environmental pollution; μi symbolizes the individual fixed effect, while δt controls for the time fixed effect; εi,t is the random disturbance term.

Secondly, to explore the mechanisms through which the digital economy impacts environmental pollution, a transmission effect model is introduced as depicted in Eqs 2 , 3 . Here, itvi,t represents a series of mechanism variables through which the digital economy influences environmental pollution.

Thirdly, to assess the moderating effect of public environmental concern on the mechanism variables, this effect is captured in Eq. 4 .

Additionally, the explanatory variables and the cross-multiplication term of each control variable with the spatial weight matrix are integrated into Eq. 1 to construct the Spatial Durbin Model (SDM), as detailed in Eq. 5 . Here, φ2 represents the spatial spillover coefficient, and W is the spatial weight matrix.

3.3 Data sources and descriptive statistics

Drawing on the approach of Zhang et al. (2023) , 30 provinces in China (excluding Tibet, Taiwan, Hong Kong, and Macau) are selected as the research sample, covering the period from 2011 to 2022. The selected provinces are shown in Figure 1 . The data for the dependent variables are sourced from the China Environmental Yearbook. Composite indicators for the dependent variables are derived from the digital finance Index of Peking University, the China Statistical Yearbook, and the respective statistical yearbooks of each province. The data for the control variables are also obtained from the China Statistical Yearbook and the provincial statistical yearbooks. Descriptive statistics for each variable are presented in Table 2 .

Figure 1 . Selected provincial samples.

Table 2 . Descriptive statistics.

4 Empirical analysis

4.1 benchmark regression.

Table 3 illustrates the results of a regression analysis on the impact of the digital economy on environmental pollution. As control variables were incrementally added to the regression model, the estimated coefficient for the core explanatory variable, the digital economy index (Dtf), consistently showed a significant negative effect. This strongly supports the hypothesis that the digital economy significantly mitigates or reduces environmental pollution, confirming Hypothesis 1 .

Table 3 . Main results.

Among the control variables, fiscal freedom’s impact on environmental pollution is also negative, suggesting that increased fiscal freedom provides local governments with more resources to combat environmental pollution. Financial development negatively correlates with environmental pollution; higher financial development levels likely channel more funds towards sustainable practices, thereby reducing pollution levels. However, the influence of infrastructure development on environmental pollution was found to be statistically insignificant. Medical care levels also negatively affect environmental pollution, implying that higher levels of healthcare lead to greater public awareness and concern for health, which in turn discourages environmental pollution. The impact of investments in science and technology on environmental pollution is also negative, reinforcing the idea that technological advancements drive energy efficiency and pollution reduction. Similarly, higher education levels correlate with reduced environmental pollution, as they foster more expertise in pollution prevention and control. The old-age dependency ratio negatively affects environmental pollution. Elderly populations, being more health-sensitive, tend to reside in areas with better environmental conditions, thus places with higher elderly care levels experience lower pollution. In contrast, the child dependency ratio has a positive impact on environmental pollution. In regions with higher fertility rates, which typically have more outdated production methods, environmental pollution is more severe.

4.2 Mechanism analysis

Hypothesis 2 asserts that the development of the digital economy impacts the level of environmental pollution through pathways such as increased government focus on environmental governance, greater investment in pollution control, and more stringent environmental administrative law enforcement. This section provides an empirical examination of these mechanisms, with regression outcomes detailed in Table 4 . Results from columns (7), (9), and (11) reveal that the digital economy substantially enhances government attention to environmental governance, boosts investment in environmental pollution control, and strengthens environmental administrative law enforcement. Furthermore, data from columns (6), (8), and (10) indicate that public environmental concern, environmental regulation level, green innovation level, and industrial structure significantly diminish environmental pollution levels. Consequently, the digital economy mitigates environmental pollution through mechanisms that influence government attention, investment in pollution control, and the enforcement of environmental laws, thus confirming Hypothesis 2 .

Table 4 . Mechanism analysis.

4.3 Analysis of the regulatory effects of public environmental concerns

Hypothesis 3 posits that public environmental concern positively moderates the relationship between the digital economy and environmental pollution, enhancing the impact of digital economy on government environmental regulation.

Table 5 , column (12), demonstrates that the impact of the digital economy on public environmental concern is significantly positive at the 1% level, indicating that the digital economy boosts public environmental awareness. Columns (13), (14), and (15) show that after incorporating the interaction term between the digital economy and public environmental concern, the digital economy positively affects local government attention to environmental protection, investment in environmental pollution control, and environmental law enforcement efforts, all significant at least at the 10% level.

Table 5 . Test of moderating effect of public environmental concern.

Moreover, the interaction term between the digital economy and public environmental concern is significantly positive, highlighting that the digital economy promotes government attention to environmental protection, environmental pollution control investment, and law enforcement efforts by increasing public awareness. In essence, the development of digital economy enhances environmental quality by fostering the synergy of public environmental concerns and prompting local governments to intensify their environmental governance efforts. This process is indicative of China’s evolving pattern of diverse and collaborative environmental governance, driven by the government with widespread public involvement, which plays a pivotal role in controlling environmental pollution and outlines the future trajectory of environmental governance. Hypothesis 3 is validated.

4.4 Spatial spillover effect test

Hypothesis 4 believes that the digital economy will have a spatial spillover effect on environmental pollution. This part examines the spatial spillover effect of the digital economy. It is mainly divided into three parts: comparative analysis of spatial distribution maps, spatial autocorrelation test, and spatial econometric regression. The specific process is as follows:

4.4.1 Comparative analysis of spatial distribution maps

Figure 2 shows the spatial distribution of the environmental pollution in 2011, 2016, and 2022. Overall, from 2011 to 2022, the environmental pollution showed a downward trend, indicating that the environmental pollution in China is improving. The provinces with a relatively high degree of environmental pollution are concentrated in the northern part of China, such as Inner Mongolia, Shanxi, and Hebei. The environmental pollution in the eastern coastal provinces is relatively low. The environmental pollution of each province shows certain characteristics of spatial agglomeration.

Figure 2 . Spatial Distribution Map of Environmental pollution.

Figure 3 shows the spatial distribution of the digital economy of various provinces in China in 2011, 2016, and 2022. It can be seen that the level of China’s digital economy has achieved relatively large development from 2011 to 2022. Moreover, the development level of the digital economy in northern provinces is lower than that in the south. The development level of the digital economy in southeastern coastal provinces is relatively high, and there is also a certain degree of spatial agglomeration in the development level of the digital economy in space. By comparing Figure 2 , it can be found that the environmental pollution of Inner Mongolia, Shanxi, Hebei and other provinces is at a relatively high position in the whole country, and the development level of the digital economy is at a relatively low position in the whole country. The environmental pollution of coastal provinces is at a relatively low level in the whole country, and the development level of the digital economy is at a relatively high position in the whole country. There is a certain correspondence in the spatial distribution between the two. In the following text, the spatial autocorrelation level of the environmental pollution and the development of the digital economy will be further tested.

Figure 3 . Spatial Distribution Map of the Digital economy.

4.4.2 Spatial autocorrelation test

The premise of the spatial econometric model is the existence of spatial correlation among the study variables. The Moran’s Index is used to test the spatial autocorrelation between the digital economy and environmental pollution from 2011 to 2022. Table 6 presents the Moran indices for both digital economy and environmental pollution levels using a geographical distance matrix. The results show significant spatial autocorrelation at the 1% level for the period studied, justifying further spatial econometric regression.

Table 6 . Results of spatial autocorrelation test.

4.4.3 Spatial econometric regressions

The outcomes from spatial measurement regressions are reported in columns (16) to (19) of Table 7 , utilizing four different spatial weight matrices: neighborhood distance, geographic distance, economic distance, and an economic-geographic nested matrix. The results across these matrices are consistent. However, the analysis focuses on the economic-geographic nested matrix shown in column (19), which combines both economic and geographic factors. Note that the spatial econometric model is uniquely characterized, particularly in column (19), rows 3 and 4, which highlight the regression coefficients for the digital economy and its interaction with the spatial weights. The significance of these coefficients primarily indicates whether the digital economy directly influences environmental pollution. However, the actual existence and extent of its spatial impacts require further analysis using spatial econometric modeling techniques, such as partial differentiation; the actual values of the coefficients themselves are less critical here. Detailed examination of the direct and spillover effects is required through partial differentiation, presented in rows 6 and 7 of column (19).

Table 7 . Spatial spillover effect test.

As evident from the third row of column 19, the direct impact of the digital economy on environmental pollution is negative, indicating that the growth of the digital economy within a region is likely to reduce its environmental pollution. Similarly, the fourth row reveals a negative coefficient for the interaction term between the digital economy and the spatial weight matrix. This suggests that while the digital economy may decrease environmental pollution within a region, it can concurrently mitigate pollution in surrounding areas as well. This may stem from the demonstrative effect of the digital economy, where as it prompts an increase in environmental regulatory efforts by local governments, neighboring governments may also face pressure from performance evaluation and public attention, leading to enhanced environmental pollution management. Consequently, when the digital economy reduces environmental pollution in a given region, it tends to have a similar effect in neighboring areas. Thus, Hypothesis 4 is confirmed.

5 Robustness test

5.1 instrumental variable methods.

Drawing on the methodology of Nunn and Qian (2014) and Zhang et al. (2023) , we use historical data on post and telecommunications from provinces in 1984 as a basis, combined with the number of Internet users in the country from 2011 to 2022, to construct an instrumental variable. This approach is chosen because the development of the digital economy is closely related to local infrastructure such as postal and telecommunication services, thus satisfying the requirement for correlation between the instrumental variable and the explanatory variables. Additionally, the impact of postal and telecommunication infrastructure on environmental pollution has become negligible over time, meeting the exogeneity requirement of the instrumental variable. Please refer to Table 8 for the relevant regression results.

Table 8 . Robustness check of instrumental variable method.

The results from Table 8 confirm that the digital economy continues to have a significant negative impact on environmental pollution, even after addressing the endogeneity issue. Furthermore, the instrumental variables cleared the LM test with F-values exceeding 10, affirming their statistical validity and appropriateness. These findings robustly support a deeper exploration of the interplay between the digital economy and environmental pollution.

5.2 Replace explained variables

We substituted the main explained variable for its subcomponents sulfur dioxide emissions, water pollutants, and solid pollutant discharge to verify the robustness of our regression analysis. The modified regression outcomes, presented in Table 9 , demonstrate that the results remain robust even after this substitution. This further solidifies the reliability of our regression findings.

Table 9 . Regression with substitute dependent variables.

5.3 Heterogeneity analysis

At present, various regions in China are at different stages of industrialization and economic development levels, which leads to differences in both environmental pollution and the development of the digital economy. Therefore, in accordance with China’s regional planning standards, specifically as shown in Figure 4 , it is necessary to analyze the sub-samples from the three regions in order to better understand these differences. The regression results are presented in Table 10 .

Figure 4 . Regional division of eastern, central and western regions.

Table 10 . Regional heterogeneity.

Table 10 reveals that the impact of the digital economy on environmental pollution is negative across eastern, central, and western regions. However, the most substantial negative impact is observed in the western region, followed by the central and the least in the eastern region. This trend may be attributed to the high level of environmental pollution and the relatively undeveloped the digital economy in the western region, resulting in the largest marginal utility of the digital economy interventions on environmental pollution. In contrast, the digital economy in the eastern region, being more developed and shows a diminishing marginal effect due to lower levels of environmental pollution. Despite this, the development of digital economy remains crucial in the eastern region, which possesses more experience in reducing environmental pollution through digital means. Therefore, the central and western regions could benefit significantly from adopting the eastern region’s strategies.

6 Conclusion and policy implications

This study constructs a digital economy development indicator system for 30 provinces spanning from 2011 to 2022, taking into account both government environmental governance and public environmental concerns as the starting points. Utilizing fixed-effect models, spatial econometric models, and an instrumental variable system, it examines the mechanisms of how digital economy development contributes to environmental pollution control. The key findings are as follows: 1) The development of the digital economy significantly reduces environmental pollution, promoting regional green transformation and development. 2) This reduction in environmental pollution is achieved through the government’s pre-event focus on environmental governance, in-event investment in pollution control, and post-event enforcement efforts. 3) Public environmental concern positively moderates the relationship between the digital economy and environmental pollution, enhancing the impact of the digital economy on government environmental regulations. 4) The impact of the digital economy on environmental pollution exhibits a spatial spillover effect, where the development of the digital economy reduces pollution in a given region while also lowering pollution levels in surrounding areas.

According to the conclusion, the following suggestions are made:

(1) Vigorously promote the development of the Dtf and improve relevant policies and safeguard systems. Promote the Dtf: strengthen the construction of infrastructure such as 5G and big data, promote the digital transformation of industry, develop new business forms such as platform economy, innovate the application of technology, improve data management and laws and policies, optimize the business environment, train digital talents, narrow the digital divide, and build a healthy and sustainable Dtf ecology.

(2) Give full play to the guiding role of the Dtf in green development through government environmental governance. We will strengthen local governments’ attention to environmental governance and enhance their environmental supervision capabilities. We will increase investment in Pol control and improve the government’s ability to improve the ecological environment. Strengthen government environmental law enforcement and administrative penalties through information and technology, so as to promote Pol reduction.

(3) Improve the construction of government public service platforms and improve the channels for the public to express environmental demands. The Dtf will guide the public to pay attention to and participate in environmental governance. With the help of digital technologies and platforms, local governments and social forces are encouraged to interact and cooperate in Pol control, and a collaborative environmental governance system with government supervision as the leading role and public participation as the auxiliary role is better established, so as to improve the public’s participation in and supervision of government environmental governance, and thus strengthen the effect of Pol control.

(4) To harness the positive spatial spillover effects of the digital economy on environmental pollution, we should enhance inter-regional communication and cooperation. Through regular dialogues, information sharing, personnel training, and exchange programs, we can facilitate the transfer of technology and expertise from advanced regions to less-developed ones. Additionally, leveraging the technological advantages of the digital economy, we should promote green digital technologies, optimize industrial layouts, and strengthen environmental oversight, ultimately achieving the coordinated development of the digital economy and environmental protection.

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: https://idf.pku.edu.cn/yjcg/zsbg/index.htm .

Author contributions

KL: Conceptualization, Software, Writing–original draft. FM: Data curation, Methodology, Writing–review and editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: digital economy, environmental pollution, environmental governance, coordinated governance, spatial spillover

Citation: Liu K and Ma F (2024) The impact of the digital economy on environmental pollution: a perspective on collaborative governance between government and Public. Front. Environ. Sci. 12:1435714. doi: 10.3389/fenvs.2024.1435714

Received: 20 May 2024; Accepted: 12 June 2024; Published: 03 July 2024.

Reviewed by:

Copyright © 2024 Liu and Ma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Fanglin Ma, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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The impact of green finance on agricultural pollution and carbon reduction: the case of china.

1. Introduction

2. literature review, 3. theoretical analysis and hypothesis, 3.1. the impact of green finance on agricultural pollution reduction and carbon reduction, 3.2. regional heterogeneity mechanism analysis, 3.3. the mediating effect of technological innovation and industrial structure optimization, 4. materials and methods, 4.1. model setting, 4.2. variables and data, 4.3. data sources, 5. results and discussion, 5.1. analysis of benchmark regression results, 5.2. heterogeneity analysis, 5.3. mechanism analysis, 5.4. robustness test and endogeneity test, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Cao, L.; Gao, J. The Impact of Green Finance on Agricultural Pollution and Carbon Reduction: The Case of China. Sustainability 2024 , 16 , 5832. https://doi.org/10.3390/su16145832

Cao L, Gao J. The Impact of Green Finance on Agricultural Pollution and Carbon Reduction: The Case of China. Sustainability . 2024; 16(14):5832. https://doi.org/10.3390/su16145832

Cao, Li, and Jiaqi Gao. 2024. "The Impact of Green Finance on Agricultural Pollution and Carbon Reduction: The Case of China" Sustainability 16, no. 14: 5832. https://doi.org/10.3390/su16145832

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Supreme Court blocks an EPA plan to curb ozone air pollution

Environmental advocates say the court’s decision in ohio v. epa shows it "is no longer neutral in cases involving environmental regulations.”.

In a ruling that court observers said was “really extraordinary” and achieved through “a procedural strangeness,” the Supreme Court on Thursday blocked a federal plan to reduce air pollution that blows across state lines. 

The 5-4 decision from the court’s conservative justices halts, for now, the Environmental Protection Agency’s “Good Neighbor” rule and its stringent smokestack emissions requirements on power plants and other industrial sources. The court ruled that the EPA failed to “reasonably explain” its policy and placed it on hold pending the outcome of more than a dozen lawsuits.

Environmental advocates said the decision will leave millions of people breathing dirtier air this summer. They also worry that future challenges to federal policies could similarly “ short-circuit the normal process of judicial review ” by appealing directly to the Supreme Court. 

“What this shows me is that this court is no longer neutral in cases involving environmental regulations,” Sam Sankar, senior vice president for programs at Earthjustice, told reporters on Thursday. “It’s actively skeptical of EPA and new environmental regulations.”

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The Good Neighbor plan was adopted to ensure compliance with a 2015 update to the Clean Air Act that tightened federal limits on ozone, a harmful pollutant and the primary component of smog. That update triggered a requirement for each state to submit a plan within three years detailing how it would reduce ozone-forming emissions from coal-fired power plants and heavy industry to protect downwind states. The law also required the EPA to craft a plan for states that failed to provide an adequate proposal.

Twenty-one states submitted plans indicating that they would do nothing, while Pennsylvania and Virginia didn’t offer one at all. In March 2023, the EPA issued its own proposal for the 23 states, prompting dozens of lawsuits in federal courts around the country.

Ohio, Indiana, and Virginia, joined by pipeline company Kinder Morgan, U.S. Steel, and others, in challenging the plan , argued that the EPA’s approach failed to consider the impact of a federal plan on each state. They also alleged that the steps needed to implement it could create economic and operational harm even as lower courts decide other lawsuits.

The justices, in a majority opinion written by Justice Neil Gorsuch, agreed. Gorsuch noted that the EPA’s plan to implement pollution reduction requirements regardless of how many states are involved was not “reasonably explained.” 

“The government refused to say with certainty that EPA would have reached the same conclusions regardless of which states were included,” he wrote.

But Justice Amy Coney Barrett argued in a strongly worded dissent that the agency “thoroughly explained” its methodology for calculating emissions reduction requirements, which depends not on the number of states included in the plan, but on cost-effective measures that can be achieved at each source of pollution. Barrett also noted that the plaintiffs and the court failed to identify how exactly the rule would differ if the number of states changed.

Sankar, who has for 25 years closely watched the Supreme Court’s decisions on environmental matters, called the ruling “really extraordinary” for two reasons. First, the EPA did in fact explain its reasoning in numerous documents. Second, the case landed on the court’s emergency docket, a lineup that until recently largely was reserved for minor procedural issues typically decided without the justices hearing oral arguments.

Zachary Fabish, senior attorney at the Sierra Club, told Grist that by hearing oral arguments and issuing so consequential an opinion on its emergency docket, the Supreme Court has created a kind of “procedural strangeness” in its decision making. He pointed out that the case had yet to be decided by the U.S. Court of Appeals for the District of Columbia Circuit, which will likely rule on the legitimacy of the Good Neighbor plan sometime next year. That means that even before the lower court’s decision, the Supreme Court has already weighed in — but without the benefit of extensive briefings, arguments, and opinions from a lower court, he said. 

Today’s ruling suggests future environmental policies could face similar challenges on the emergency docket, said Sankar. “It’s really hard to say that there are any rules that aren’t subject to this kind of attack.”

Clean air advocates highlighted another glaring omission from the court’s opinion: It made no mention of the public health toll of the pollution on downwind states. Ozone forms in high temperatures and sunlight, making summer months particularly conducive to its formation. As Fabish puts it, “The hotter the summer, the worse the ozone season” — a foreboding sign as much of the country broils under relentless heat . Research has shown that ozone increases the risk of life-threatening conditions like asthma attacks, especially among children, older adults, people who work outside, and people with respiratory and other illnesses.

Last summer, data collected by the EPA showed that from May to September, the Good Neighbor rule — which at the time was in effect in 10 states, including Illinois, New York, and Ohio — successfully drove down ozone-forming emissions by 18 percent . “Staying this rule threatens the progress that happened last ozone season when the rule was partially in effect,” Fabish said.

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Summer airborne research targets Rocky Mountain ozone pollution

• July 5, 2024
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NOAA scientists and several partners are taking a fresh look at persistent air pollution problems bedeviling the nation’s two largest Mountain West metro regions, Denver and Salt Lake City, from the ground, in the air and from space, during new research projects launched this month. 

For two weeks in July, NOAA scientists, joined by NASA scientists and state officials, will be fanning out across northeastern Colorado’s Denver-Julesburg basin, one of the most densely drilled oil and gas regions in the country. Specialized instruments mounted on airplanes, ground vehicles, and stationary sites will allow researchers to measure emissions of greenhouse gases and the air pollutants that contribute to the high summer ozone pollution that has long plagued the Denver Metropolitan Area. 

Researchers intend to quantify emissions from oil and gas operations, agriculture, industry, and urban sources, assess to what degree they’ve changed from previous studies, and evaluate their contributions to summertime ozone in the Front Range. Findings will help the Colorado Department of Public Health and Environment, which is supporting the project, determine the effectiveness of state policies to reduce ozone levels that currently exceed national air quality standards.

In 2022, the EPA downgraded the Denver metro and northern Front Range regions to “severe” nonattainment status under the current ozone standard, and established a July 2027 deadline for coming into attainment.  

“It’s been exactly 10 years since the last major air quality study took place in the Denver metro area,” said Sunil Baidar, a research scientist with NOAA’s Chemical Sciences Laboratory (CSL), which is leading the twin studies. “We need to understand the changing landscape of emission sources to help the state develop strategies to meet air quality standards across the Denver metro area and northern Colorado Front Range.”

Investigating pollution from the surface to space

The Colorado project pairs the NOAA Twin Otter and NASA King Air research aircraft, which will be joined by scientists aboard several mobile platforms, including: NOAA‘s Air Resources Laboratory Air Resources Car , as well as CSL’s Pick-Up based Mobile Atmospheric Sounder , Tunable Optical Profiler for Aerosol and oZone (TOPAZ) lidar, and Mobile Laboratory . Both the airborne and mobile platforms will be equipped to make chemical measurements of methane, carbon monoxide, ethane, nitrogen oxides and ozone, along with Doppler lidar systems to measure winds at different elevations. 

This graphic depicts how a tiered observing system composed of surface, airborne and satellite assets is used to evaluate and monitor U.S. oil and gas and urban emissions of methane and air pollutants. Credit: NOAA Chemical Sciences Laboratory

The Colorado Front Range is an ideal first target for deploying this tiered observations methodology, said Steven Brown, CSL’s Tropospheric Chemistry program leader. While the Twin Otter flies chemical instruments to directly measure methane concentrations at flight altitude, the King Air is equipped with a downward-looking optical methane imager, known as AVIRIS-3 , that is capable of mapping individual methane plumes and leaks. Flying overhead, three different satellites will remotely measure methane at varying spatial scales.

Michael Ogletree, director of CDPHE’s Air Pollution Control Division , said the state anticipates the NOAA study will provide valuable information quantifying greenhouse gas emissions that cause climate change, and identifying factors contributing to summertime ground-level ozone pollution. 

“Measuring the concentration of ground-level ozone and precursor pollutants across the Front Range will help the division make data-driven policy decisions and advance real-time forecasting to keep Coloradans informed about ozone pollution levels,” Ogletree said.

The Denver project will be immediately followed by another multi-platform air quality investigation on summer ozone pollution in the greater Salt Lake City and Wasatch Front region of Utah. 

Salt Lake Summer Ozone Study

The Utah study, in late July and August, will use many of the same mobile platforms, augmented with measurements from several fixed ground sites. The goal of the project is to measure the type and distribution of pollutants that cause ozone development, and the atmospheric boundary layer that keeps pollutants trapped near the Earth’s surface. The Salt Lake metro region is also in non-attainment of the EPA ozone standard.

The NOAA Air Resources Laboratory’s Air Resources Car’s displays real-time air quality measurements during a previous research project. Credit: Air Resources Laboratory

The pair of 2024 projects represent the first phase of a five-year campaign, called Airborne and Remote sensing Methane and Air Pollutant Surveys, or AirMAPS for short. Led by NOAA Research and NOAA’s National Environmental Satellite and Information Service, six discrete projects between 2024 and 2028 will survey the majority of U.S. oil and gas basins between 2024 and 2028 to assess current oil and gas methane and air pollutant emissions 

The investigation will also evaluate other greenhouse gases and air pollutants generated by agriculture, landfills, coal mining and wetlands. The mission will demonstrate the value of using integrated, tiered greenhouse gas observing systems incorporating comprehensive airborne surveys, satellite- and aircraft-based remote sensing, and ground-based observations not only to quantify emissions from oil and gas basins, but also impacts from oil and gas use in urban testbed areas . Findings will support development of the Greenhouse Gas And Air Pollutants Emissions System , a joint effort by the Chemical Sciences Laboratory and the Department of Commerce’s National Institute of Standards to develop the capability to measure and model U.S. emissions of greenhouse gases and hazardous air pollutants.

When complete, scientists will have surveyed the source regions for more than 90% of currently estimated oil and gas methane emissions, and a similar fraction of oil and gas production. It will be the most comprehensive airborne methane and air pollutant assessment to date. Data will make a major contribution to the new U.S. Greenhouse Gas Center , an effort led by NASA, EPA, NIST, and NOAA to aggregate existing and new scientific data to better understand greenhouse gas cycling by human-caused and natural processes. 

“AirMAPS is a large partnership among multiple federal agencies, states, industry and universities bringing together state-of-the-art tools to better understand methane emissions and their sources,” Brown said. “The outcome may have immediate benefits for strategies to improve poor air quality and mitigate climate change.”

For more information, contact Theo Stein, NOAA Communications at [email protected] , or Chelsea Thompson, Chemical Sciences Laboratory, at [email protected].

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INVESTING IN AMERICA: Biden-Harris Administration Strengthens Transit Manufacturing Industry with $1.5 Billion from Bipartisan Infrastructure Law to Put More American-Made Buses on the Road

The third round of historic federal funding will help transit agencies serve more riders, reduce air pollution, and train workers on new technologies

WASHINGTON – The U.S. Department of Transportation's Federal Transit Administration (FTA) today announced approximately $1.5 billion in funding to support 117 projects that will improve public transportation in 47 states. Under the President’s historic Bipartisan Infrastructure Law, FTA has awarded nearly $5 billion in the past three years to replace and modernize transit buses on America’s roadways, building new technology with American workers. U.S. factories will produce more than 4,600 of these new buses.

"Today, another 117 communities across 47 states are receiving the good news that their transit buses are being modernized and their commutes improved through President Biden's Bipartisan Infrastructure Law," said U.S. Transportation Secretary Pete Buttigieg . "The Biden-Harris Administration is helping agencies replace old buses running on dirtier, expensive fuels by delivering modern and zero-emission buses, manufactured by American workers, that will connect more people to where they need to go."

"Investing in low-emission and no-emission transit buses has emerged as a transformational pillar of President Biden's transportation emissions reductions strategy. This innovative and common-sense approach is already bolstering American manufacturing and creating good-paying union jobs – and it will safeguard the planet for future generations," said Assistant to the President and National Climate Advisor Ali Zaidi . "By modernizing transit with improved clean energy technologies, we will also boost the longevity of our mass transportation system, reduce traffic congestion, and clean up the air we breathe in the neighborhoods and communities that line our roads and highways." 

This funding continues the Biden-Harris Administration's historic federal investments in the nation’s bus systems and the transit workforce. About 80 percent of the buses being funded will run on zero or low emission technology, reducing air pollution and helping meet the President’s goal of zero emissions by 2050. These programs also advance President Biden’s Justice40 Initiative , which sets the goal that 40 percent of the overall benefits of certain federal investments in climate, clean energy, clean transportation, and other areas flow to disadvantaged communities that are marginalized by underinvestment and overburdened by pollution.

"Thanks to the Bipartisan Infrastructure Law, we are creating new opportunities to improve the lives of millions of Americans who rely daily on buses," said FTA Acting Administrator Veronica Vanterpool . "These grants will help deliver cleaner and greener transportation, designed to reach everyone, and to work for everyone, particularly in places that haven’t received enough resources in the past."

Federal support for bus projects over the last several years has been instrumental in improving public transit and helping communities advance their climate goals.

For this year's selections, FTA prioritized awards that will help strengthen U.S. bus manufacturing , stabilize the cost of new buses, and accelerate the delivery of new vehicles. Of the 117 projects selected, 47 projects totaling $817 million are from applicants that committed to procuring standard model buses or using a joint procurement. Buying standardized bus models without customization can shorten manufacturing timelines and contain costs. By prioritizing these awards, FTA is encouraging lower costs and accelerated vehicle delivery that will result in more American-built buses getting on the road faster.

See all FY24 projects here . 

Examples of projects receiving FY24 funding include:

• New Jersey Transit will receive approximately $99.5 million to build a charging facility with a solar canopy at its Meadowlands Bus Garage. This project will allow New Jersey Transit to shelter, charge, and support the deployment of battery-electric buses with renewable energy while increasing service and advancing environmental justice throughout the state.  
• The Sacramento Regional Transportation District will receive $76.8 million to buy up to 29 new hydrogen fuel cell buses to replace older buses, modernize a maintenance facility, and initiate a workforce development program. The project will improve service, reliability, and air quality throughout the greater Sacramento area.  
• The Colorado Department of Transportation, on behalf of the Roaring Fork Transportation Authority (RFTA), will receive $32.8 million to modernize its Glenwood Springs Operations and Maintenance Facility to support its future zero-emission bus fleet. This project will help RFTA, which serves three counties and eight municipalities in rural central Colorado, achieve its goal of a 100% zero-emission bus fleet by 2050.  
• The Central Florida Regional Transportation Authority (LYNX) in Orlando will receive $27.6 million to buy up to 30 compressed natural gas buses to replace older diesel buses on routes throughout Central Florida. This project will support LYNX's efforts to transition its entire fleet to a combination of low- and zero-emission vehicles by 2028, as well as provide more efficient and reliable service to its riders.  
• The Shoshone Bannock Tribe will receive $722,400 to buy five buses and two vans to replace older vehicles. The new vehicles will enable the tribe to provide much-needed bus service on the Fort Hall Reservation and into Pocatello, Blackfoot, and Power County, Idaho.

The Grants for Buses and Bus Facilities program provides federal funding for transit agencies to buy and rehabilitate buses and vans and build and modernize bus facilities. The Bipartisan Infrastructure Law provides nearly $2 billion through 2026 for the program. For Fiscal Year 2024, approximately $390 million was available for grants under this program.

FTA's Low-and No-Emission program makes funding available to help transit agencies buy or lease U.S.-built low- or no-emission vehicles, including buses and vans, make facility and station upgrades to accommodate low- or no-emission vehicles, and buy supporting equipment like battery electric charging. The Bipartisan Infrastructure Law provides $5.6 billion through 2026 for the Low-No Program – more than ten times greater than the previous five years of funding. For Fiscal Year 2024, approximately $1.1 billion was available for grants under this program.

In addition to investing in the future of transit, the awards announced today also invest in America's workers. The zero-emission bus grants include support for transit agencies to train their workers to drive and maintain buses powered by new technology.

In response to the Notice of Funding Opportunity , FTA received 477 eligible project proposals totaling $9 billion in requests.