Air Pollution Prevention Research Articles

P(VDF-TrFE)/BaTiO3 Nanofibrous Membrane with Enhanced Piezoelectricity for High PM0.3 Filtration and Reusable Face Masks

In the background of air pollution and regular COVID-19 prevention, personal protective masks are necessary for our daily life. However, protective masks with high PM0.3 filtration usually have poor air permeability and are mostly disposable, leading to a heavy burden on the environment. In this work, a reusable membrane based on piezoelectric poly(vinylidene fluoride-trifluoroethylene) [P(VDF-TrFE)] nanofibers embedded with BaTiO3 nanoparticles (BTO NPs) was developed. The P(VDF-TrFE)/BTO composite nanofibers not only have enhanced piezoelectricity and surface polarity but also have reduced diameters that could be beneficial for electrostatic adhesion, pole-polar interactions, and mechanical sieving to increase the PM0.3 capture capacity. Moreover, the BTO NPs also improved the charge storage capacity of the composite membrane, which could further enhance the PM0.3 filtration efficiency after corona treatment. The piezoelectric mask based on P(VDF-TrFE)/BTO composite nanofibers has high filtration efficiencies of 96% for PM0.3 and 98% for bacteria, while the pressure drop was only 182 Pa, which is lower than the commercial N95 standard of 343.2 Pa. Furthermore, the piezoelectric mask has a long and stable filtration performance after 5 cycles of 75% alcohol disinfection, demonstrating that the P(VDF-TrFE)/BTO composite membrane has a potential application in personal protective masks with comfortable and reusable properties.

Evolutionary Game Mechanism of Governmental Cross-Regional Cooperation in AirPollution Management

Intergovernmental collaboration is essential for regionally coordinated air pollution prevention and control. An evolutionary game model of local government behavior strategy selection taking into account the fixed cost of the local government, the total amount of network production factors, and the total amount of total network factors of production that can be moved is built based on the social capital theory in order to realize the cross-border collaborative control of regional air pollution. The issue of an intergovernmental cooperation framework for collaborative prevention and control of large-scale air pollution is addressed from the standpoint of the multi-stakeholder “benefit–cost” drive. Additionally, the major variables influencing the behavioral approach selection for intergovernmental cooperation are considered. The initial sensitivity of the evolution path of the local government behavioral strategy is also analyzed. The results of this study are: (1) The primary elements impacting intergovernmental cooperation on joint prevention and control of air pollution are fixed costs and fixed benefits, and reducing the fixed costs of such cooperation in an appropriate manner without compromising local governments’ pollution control can do so. (2) Under the assumption that local governments have fixed expenses, the total amount of network factors of production and total network factors of production that can be moved factors of production have a direct impact on intergovernmental cooperation. When local governments’ fixed costs are constant, they are more likely to choose the cooperative behavior option if the sum of their network production factors and total network factors of production that can be moved is higher. (3) The initial probability of cooperation among the three parties and the total amount of production factors have an impact on the system’s ESS when local governments in the area have equal total production factors. The study’s findings can offer theoretical justification for the “profit-driven” intergovernmental coordination of joint prevention and management of air pollution.

Characteristics of Ozone Pollution and High-impact Meteorological Factors in Urban Cities: A Case of Suzhou

The problem of urban ozone (O3) pollution has become prominent in recent years. However, the meteorological factors associated with O3 pollution remain unclear. Analyzing the characteristics of O3 pollution in Suzhou, as a typical urban city, and exploring the high-impact meteorological factors with O3 pollution are crucial to the prevention and control of air pollution in this region. This study used correlation analysis and machine learning methods to analyze the variation in O3 concentration and the relationship between meteorological driving factors in Suzhou based on the O3 concentration data provided by Suzhou Environmental Monitoring Center and the contemporaneous meteorological observation data in Suzhou from April to September in 2015 to 2020. The results showed that: ① O3 pollution exceeding the standard rate was more than 20% in ozone seasons during the past six years; further, pollution days of O3 and the number of pollution days of O3 as the primary pollutant increased yearly. Evidently, the problem of O3 pollution has become increasingly prominent. ② The diurnal variations in O3 were unimodal with the valley point at 07:00 and the highest peak between 15:00 and 16:00. Similar trends were found in diurnal variations of both air temperature and solar radiation, but the daily highest peak came earlier than that of O3. The results also showed an apparent weekend effect of O3 concentration in 2017 and 2019 and a significant correlation between O3 concentration and solar irradiance during the week. In addition, the monthly variation in O3 concentration and pollution exceeding the standard rate was bimodal. ③The occurrence of ozone pollution was affected by various meteorological conditions. The maximum number of days appeared when daily sunshine hours lasted longer than 7 hours, with a daily maximum air temperature around 30℃, solar irradiance ranging from 350 to 440 kW·m-2, and relative humidity ranging from 50% to 75%, at which time the intensity of pollution was the strongest. When the wind speed of easterly wind was less than 1.5 m·s-1, or the wind speed of southwest wind was less than 3.5 m·s-1, moderate ozone pollution occurred. ④ An optimal prediction model of O3 concentration was established based on machine learning, which had good predictive ability for O3 concentration in April, May, July, and September but did not perform well when O3 concentration exceeded 200 μg·m-3. Meanwhile, it was found that solar radiation had the most obvious effect on O3 concentration, followed by relative humidity, whereas the temperature and wind were less important than the former two factors.

Characters of Particulate Matter and Their Relationship with Meteorological Factors during Winter Nanyang 2021–2022

The purpose of this study is to investigate the air quality levels of Nanyang city according to Chinese air quality standards. Therefore, in this study, fine particulate matter (PM2.5), coarse particulate matter (PM10), and total suspended particulate (TSP) were analyzed from 19 November 2021 to 19 March 2022 in Nanyang city. The results show that the average concentrations of PM2.5, PM10, and TSP were 106.47 µg/m3, 137.32 µg/m3, and 283.40 µg/m3, respectively. The numbers of days that meet the national secondary air quality standard of 24-h average concentrations were 29.75% for PM2.5, 63.64% for PM10, and 63.64% for TSP, indicating that most of the time, the air quality of Nanyang city remains polluted in winter, especially with more contributions of PM2.5 compared to PM10 and TSP. The higher concentrations were observed between 07:00 and 08:00, suggesting that vehicular emissions can be a major cause of air pollution in Nanyang city. The results also show a significant positive correlation between particulate matter and relative humidity, and a weak correlation with temperature and wind speed, which suggests that higher relative humidity increases the formation of particulate matter. This study can provide theoretical support for the local government to formulate air pollution prevention and control policies for Nanyang city.

Evolution of PM2.5 Chemical Composition and Sources in Nanjing During the Implementation of the APPCAP

To acquire a deeper understanding about the impact of the Air Pollution Prevention and Control Action Plan (APPCAP) on ambient fine particulate matter (PM2.5), measurements of PM2.5 chemical components were conducted in the urban area of Nanjing during the implementation of the APPCAP. Evolution characteristics of several properties of the PM2.5 were studied, including the concentrations, proportions, and enrichment factors of major PM2.5 components and the acidity of the PM2.5. In addition, the source apportion of the PM2.5 was analyzed using the positive matrix factorization (PMF) model. The results showed that during the implementation of the APPCAP, the proportion of sulfate (SO42-) in PM2.5 decreased, whereas the proportions of nitrate (NO-3) and ammonium (NH+4) increased significantly together, from 25.58% in 2014 to 37.15% in 2017. The concentrations of carbon components (OC and EC) decreased in the early stage of the APPCAP; however, the decreasing trend slowed down in the later stage. In particular, the proportion of organic carbon (OC) increased, especially for the secondary organic carbon (SOC). The acidity of the PM2.5 decreased significantly, with the molar equivalent ratio of cations and anions increasing from 0.83 in 2014 to 1.13 in 2017. The contributions of coal combustion and the metallurgical and petrochemical industry to the PM2.5 generally showed a downward trend, whereas the contribution of vehicle emissions increased rapidly, from 8.78% in 2014 to 13.92% in 2017. The contribution of general fugitive dust fluctuated greatly, but that of soil fugitive dust showed an upward trend. This study suggested that the direct PM2.5 emissions, such as those from coal combustion and industries, were effectively controlled during the implementation of the APPCAP; however, the contribution of secondary aerosols became increasingly prominent, which indicates that future policies of PM2.5 pollution control should focus on emission reduction of the precursors such as NOx, NH3, and VOCs. In addition, the effects of increasing alkalization of the PM2.5 and enhanced oxidizability of the atmosphere on the PM2.5 pollution also require further attention.

Characteristics and Sources of Water-Soluble Inorganic Ions in PM2.5 in Urban Nanjing, China

In this study, the water-soluble inorganic ions (WSIIs) composition of fine particulate matter (PM2.5) was measured in the northern Nanjing city from 2015 to 2021. NH4+, NO3− and SO42− concentrations dominated in total WSIIs (Na+, NH4+, K+, Mg2+, Ca2+, Cl−, NO3− and SO42−), accounting for 87.8%. The nitrate with highest average concentration among all ions was 11.0 μg·m−3. Total WSIIs concentrations were higher in winter and lower in summer, with the highest levels in December (45.6 μg·m−3) and the lowest levels in August (15.1 μg·m−3). NO3−/SO42− was higher than 1, indicating the important contribution of mobile sources. The aerosols exhibited a weak acidic by the molar ratio of water-soluble anions and cations. Positive matrix factorization (PMF) analysis results showed that secondary nitrate and sulfate were the major pollution sources in December 2016 and 2020. The contribution of secondary nitrate in 2020 increased by 47.6% compared to 2016, while that of secondary sulfate decreased by 42.4%. The potential source contribution results demonstrated that for secondary aerosol concentrations, the contribution of regional transport from north of Anhui increased, while the contribution of local emissions decreased. The results from this study could contribute to the better prevention and control of regional air pollution in the future.

Public Concern about Haze and Ozone in the Era of Their Coordinated Control in China.

In China, due to the implementation of the Action Plan for Prevention and Control of Air Pollution (APPCAP), the concentrations of PM2.5 (fine particulate matter) and severe haze in most cities have decreased significantly. However, at present, haze pollution in China has not been completely mitigated, and the problem of O3 (ozone) has become prominent. Therefore, the prevention and control of haze and O3 pollution have become important and noticeable issues in the field of atmospheric management. We used the Baidu search indices of "haze" and "ozone" to reflect public concerns about air quality and uncover different correlations between level of concern and level of pollution, and then we identified regions in China that require public attention. The results showed that (1) over the last decade, the search index of haze had a rapid trend of variation in line with changes in haze pollution, but that of O3 had a relatively slowly increasing trend; (2) the lag days between the peaks of public concern and the peaks of air pollution became increasingly shorter according to daily data analysis; and (3) 96 polluted cities did not receive sufficient public attention. Although periods of heavily haze-polluted weather, which affects visibility, have generated much public concern, periods of slight pollution have not received enough public attention. Public health protection and environmental participation regarding these periods of slight pollution in China deserve appropriate levels of attention.

Prevention of air pollution during openpit mining of ore deposits

Prevention of air pollution during openpit mining of ore deposits

A satellite-driven model to estimate long-term particulate sulfate levels and attributable mortality burden in China

Ambient fine particulate matter (PM2.5) pollution is a major environmental and public health challenge in China. In the recent decade, the PM2.5 level has decreased mainly driven by reductions in particulate sulfate as a result of large-scale desulfurization efforts in coal-fired power plants and industrial facilities. Emerging evidence also points to the differential toxicity of particulate sulfate affecting human health. However, estimating the long-term spatiotemporal trend of sulfate is difficult because a ground monitoring network of PM2.5 constituents has not been established in China. Spaceborne sensors such as the Multi-angle Imaging SpectroRadiometer (MISR) instrument can provide complementary information on aerosol size and type. With the help of state-of-the-art machine learning techniques, we developed a sulfate prediction model under support from available ground measurements, MISR-retrieved aerosol microphysical properties, and atmospheric reanalysis data at a spatial resolution of 0.1°. Our sulfate model performed well with an out-of-bag cross-validationR2 of 0.68 at the daily level and 0.93 at the monthly level. We found that the national mean population-weighted sulfate concentration was relatively stable before the Air Pollution Prevention and Control Action Plan was enforced in 2013, ranging from 10.4 to 11.5 µg m−3. But the sulfate level dramatically decreased to 7.7 µg m−3 in 2018, with a change rate of −28.7 % from 2013 to 2018. Correspondingly, the annual mean total non-accidental and cardiopulmonary deaths attributed to sulfate decreased by 40.7 % and 42.3 %, respectively. The long-term, full-coverage sulfate level estimates will support future studies on evaluating air quality policies and understanding the adverse health effect of particulate sulfate.

Predicting the Level of Ecological Safety for Man-made Objects

The authors explored the utilization of simulation models as a means to ensure environmental safety, using the industrial hub of Kemerovo as an illustrative example. The article analyzes the factors that have contributed to the deterioration of the environment in the region for decades. It has been established that in terms of the overall percentage distribution of emissions from stationary sources, energy enterprises (73.0%), chemical and petrochemical industries (4.7%), and black metallurgy enterprises (7.8%) are leading in the city of Kemerovo. Simulation modelling has shown that the cause of high concentrations of harmful substances in the atmosphere of Kemerovo is due to the negative factors of industrial and household activities and their impact on environmental safety. High correlation and sensitivity coefficients indicate a lack of new available technologies in the region's industry and transport that could prevent air pollution. The forecasting model has indicated a potential two, three or even greater increase in emissions. For example, in the long-term perspective, by 2063, manufacturing emissions could potentially increase by 35 times, leading to irreversible ecological consequences. Extreme pollution and depletion of natural resources could make living in this region impossible.

Research on the Treatment of Regional Air Pollution

According to the change of air quality in the Beijing-Tianjin-Hebei region, this paper analyzes the pollution characteristics of the air environment in this region, constructs the air quality evaluation model, and finds that the main pollution sources affecting the air quality in Beijing-Tianjin-Hebei region are coal combustion and automobile exhaust. By using the flow-diffusion equation, the air pollution concentration gradients of the Second Ring Road, the Fourth Ring Road, and the Sixth Ring Road in Beijing are given. Finally, this paper also puts forward specific countermeasures for the prevention and control of air pollution.

Study on the Relationship between Economic Growth and Air Pollution in the Pearl River Delta Region

To solve the problem of balancing the relationship between economic growth and air quality in the pearl river delta (PRD) region, based on selecting the indicators characterizing the environmental quality of the PRD region (i.e., the concentrations of four pollutants such as O3, SO2, NO2, and PM10) and the GDP of its three major industries during 2006-2021, this paper established a multiple linear regression model of the relationship between economic growth and air pollution in the PRD region, using E-Views and SPSS software. The results show that: the growth of the GDP in primary and tertiary industries in the PRD region will lead to the reduction of the concentration of air pollution; the growth of the GDP in secondary industries will lead to an increase in the concentration of air pollution. The suggestions based on the results are as follow: shifting the focus of industrial development, paying attention to the prospect of tertiary industry development, and continuously promoting the prevention and treatment of air pollution.

Multi-layer long short-term memory (LSTM) prediction model on air pollution for Konya province

One of the main problems of the developing and changing world is air pollution. In addition to human causes such as population growth, increase in the number of vehicles producing exhaust emissions in line with the population, development of industry, natural causes such as forest fires, volcano eruptions and dust storms also play a role in increasing air pollution. Air pollution has become a bigger problem that reduces the quality of life of living beings and causes various lung and heart diseases due to reasons such as the growing proximity of settlements to industrial zones due to population growth, the increase in the number of individual vehicles, and zoning works carried out by ignoring air quality. Both international organizations and local authorities take various measures to control and prevent air pollution. In Turkey, necessary legal arrangements have been made within the scope of these measures and air quality monitoring stations have been established. The task of these stations is to measure pollutants such as PM10, CO, SO2 together with meteorological data such as air temperature, humidity, wind speed and direction. In this study, a prediction model for the future concentrations of PM10, CO and SO2 pollutants using the measurement data from three different air quality monitoring stations in Konya between January 2020 and January 2021 was realized with a multi-layer Long Short Term Memory (LSTM) artificial neural network. The Root Mean Square Deviation (RMSE) and Mean Absolute Percentage Error (MAPE) methods was used to calculate the performance of the study. As a result of the study, it is observed that the multi-layer LSTM architecture is more successful than the single-layer architecture.

Deep Learning Based Air Quality Prediction: A Case Study for London

Although states take various measures to prevent air pollution, air pollutants continue to exist as an important problem in the world. One air pollutant that seriously affects human health is called PM2.5 (particles smaller than 2.5 micrometers in diameter). These particles pose a serious threat to human health. For example, it can penetrate deep into the lung, irritate and erode the alveolar wall and consequently impair lung function. From this, the event PM2.5 prediction is very important. In this study, PM2.5 prediction was made using 12 models, namely, Decision Tree (DT), Extra Tree (ET), k-Nearest Neighbourhood (k-NN), Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) models. The LSTM model developed according to the results obtained achieved the best result in terms of MSE, RMSE, MAE, and R2 metrics.

THE IMPACT OF AIR QUALITY AND METEOROLOGICAL CONDITIONS INCLUDING VISIBILITY ON TOURISM: THE CASE OF ZAKOPANE (POLAND)

The aim of the study was to investigate the impact of air pollution and meteorological conditions on visibility in Zakopane, the most popular tourist destination in southern Poland. To achieve this objective, correlation analysis, multiple linear regression analysis and random forests were used. In addition, an analysis was performed of the occurrence of episodes of elevated pollutant concentrations and basic statistical characteristics of visibility, meteorological conditions and air pollution. Meteorological parameters (air temperature, relative humidity, total precipitation, wind speed, atmospheric pressure and visibility) and concentrations of air pollutants; particulate matter (PM₁₀) and gaseous pollutants (SO₂, NO₂) were recorded from 2010 to 2019. The data came from a monitoring station located in Zakopane-MpZakopaSien. It was found that high concentrations of air pollutants SO₂ and PM₁₀, along with relative humidity (RH), were the most important parameters affecting visibility limitation in Zakopane. Concentrations and indirectly also visibility were largely influenced by increased automobile traffic due to tourist activity, as well as emissions from apartment/building heating (combustion of various fuels). Understanding the relationship between air pollutant concentrations, meteorological conditions and visibility is a prerequisite and the basis for the scientific formulation of air pollution prevention and control policies in places where monitoring is particularly important.

Pengelolaan Sumber Daya Alam dan Akibat Hukum Bagi Lingkungan Hidup Perspektif Hukum Lingkungan Internasional

Introduction: Management of natural resources that is not in accordance with international problems such as air pollution. Pollution that occurs in the air environment due to forest fires that occurred in Indonesia, especially in 2019 in Riau Province, is not in line with the principles of international environmental law.Purposes of the Research: The purpose of this research is to analyze and find out about the prevention of air pollution and the legal consequences of managing natural resources that pollute the air environment based on international environmental law. Methods of the Research: The research method in this study is a normative legal research, using a law approach and a conceptual approach. Sources of research data include primary, secondary, tertiary legal sources. Data collection techniques in the form of literature study. Processing and analysis of legal materials used in writing is using qualitative analysis method.Results of the Research: I The results of this study that the management of natural resources is carried out not in accordance with the principles of international law, causing air pollution which has a negative impact on the State of Indonesia and also for the State of Malaysia, Singapore. From this research, it is also not allowed to take preventive action through supervision carried out through the government of the State of Indonesia.

Impact of the Digital Economy on PM2.5: Experience from the Middle and Lower Reaches of the Yellow River Basin.

The development of the digital economy holds great significance for alleviating haze pollution. To estimate the impact of the digital economy on haze pollution, this paper explores the spatiotemporal evolutionary characteristics of the digital economy and PM2.5 concentration in the middle and lower reaches of the Yellow River Basin from 2011 to 2019 and conducts regression analysis by combining a fixed effect (FE) model and the spatial Durbin model (SDM). Moreover, this study divides the mitigation effect of haze pollution into a direct effect and a spatial spillover effect, and it further analyzes the mechanism from the perspectives of technological innovation and the industrial structure. The empirical results show that the development level of the digital economy increases year by year and that the concentration of PM2.5 decreases year by year. The digital economy level and PM2.5 concentration in the downstream region are higher than those in the middle region, and the digital economy is negatively correlated with haze pollution. Similarly, the spatial spillover effect of the digital economy is conducive to curbing haze pollution. The robustness test also supports this conclusion. In addition, there is regional heterogeneity in the impact of the digital economy on haze pollution. The direct effect and spatial spillover effect of the digital economy on haze pollution in the downstream region are greater than those in the middle region. This study suggests that to realize air pollution prevention and control, it is necessary to strengthen the construction of digital infrastructure and create a good digital economy development environment based on local conditions. Encouraging the development of digital technological innovation and promoting industrial digital transformation hold great significance for alleviating haze pollution.

Study on the synergistic effect of air pollution prevention and carbon emission reduction in the context of "dual carbon": Evidence from China's transport sector

The transportation sector has significantly contributed to greenhouse gas and air pollutant emissions. Based on the Kaya constant equation and the LMDI decomposition model, A synergistic reduction effect model of transportation tailpipe Environmental negative externalities emissions is constructed to adopt the inter-provincial panel data of China from 2005 to 2019. The synergistic reduction emission impact and indirect drivers between air pollution and carbon emission are analyzed. The results show that the drivers of the synergistic impact are mainly energy efficiency and industrial structure. Significant synergistic emission reduction effects exist in eastern China, western China, medium-sized transportation markets, and less developed tourism regions. Further, increasing environmental regulation can improve the synergistic reduction impact of the transportation sector's air pollutants and carbon emissions. This paper provides theoretical support for winning the battle of pollution prevention and control and achieving the "dual carbon" goal in the new era.

Spatial heterogeneity and driving factors of aerosol in Western China: Analysis on multiangle implementation of atmospheric correction–aerosol optical depth in Xinjiang over 2001–2019

AbstractKnowledge of aerosol dynamics is essential to combating atmospheric aerosol pollution, and there is a growing interest in aerosol changes and their drivers. However, the effects of interactions between natural and anthropogenic drivers of aerosol are not well understood. Here, we analyse changes in aerosol optical depth (AOD) in Xinjiang, China using the multiangle implementation of atmospheric correction aerosol products over 2001–2019 and investigate the driving factors of aerosol dynamics using a random forest (RF) and a geographical detector. The results show the dominant AOD changes in the quasi‐period of 3.21 months, 7.86 months, 1.19 years, for seasonal, half‐year, and interannual variations of aerosols. The increasing and then decreasing nonlinear trends were observed in the interannual AOD variation during 19 years period. The importance ranking results of the two models indicated that meteorological factors dominated the spatial distribution of AOD in Xinjiang (72.73% for RF and 65.78% for the geographic detector), and that interactions between factors enhanced the explanatory power in AOD changes. In addition, the influence of anthropogenic factors on AOD was increasing in North Xinjiang, and the influence of precipitation and population on AOD was increasing in East Xinjiang. However, the influence of drivers on AOD in South Xinjiang was basically constant over time, showing the spatially heterogeneous relationship between AOD and drivers. This study emphasized the spatial heterogeneity and driving factors of small‐scale aerosols in arid regions and so can guide targeted air pollution prevention and control in local areas.

Population Aging Driven Slowdown in the Reduction of Economic Cost-Attributed to PM2.5 Pollution after 2013 in China.

Since seniors are more susceptible to ambient fine particulate matter (PM2.5), the high economic cost to protect the aged population from PM2.5 exposure is expected. Significant efforts have been made in China to mitigate PM2.5 since 2013 under the Air Pollution Prevention and Control Action (APPCA) Plan, which remarkably reduced PM2.5 contamination and its associated economic and health burdens. However, to what extent population aging could influence the economic benefits from the APPCA Plan is unclear. Here, we estimate five driving factors contributing to the economic cost of mortality attributable to PM2.5 pollution. The results show that the economic cost attributed to PM2.5 pollution increased from 1980 to 2013 and decreased from 2013 to 2019 in China, benefiting from the APPCA Plan. Since 2013, population aging becomes the most significant positive driver that almost offsets declining economic cost from significantly declining PM2.5. Rapid aging has become an enormous burden to PM2.5-associated health and economic loss. Our findings suggest that we should further improve air quality and enhance health care for the elderly population.