Pollution Control Policies Research Articles

The evolutionary mechanism of haze collaborative governance: novel evidence from a tripartite evolutionary game model and a case study in China

The topic of haze collaborative governance has become a hot policy issue attracting worldwide attention. However, existing studies do not pay enough attention to haze collaborative governance in the process of central environmental protection inspection (CEPI). This paper employs a tripartite evolutionary game model to explore the evolutionary mechanism of haze collaborative governance among China’s central and local governments and verifies its findings further through a case study of CEPI for air pollution in Beijing-Tianjin-Hebei and surrounding regions. The results show that haze collaborative governance is mainly affected by the performance evaluation system, policy implementation cost, clean government construction, industrial transfer trend, fiscal subsidy, environmental inspection cost, environmental accountability, and public participation. The implementation of CEPI is conducive to haze collaborative governance among central government and local governments. To promote the full implementation of air pollution control policies, the local governments need to optimize the performance evaluation system by considering economic development and environmental protection, reduce policy implementation costs by cost-sharing, and strengthen clean government construction by preventing rent-seeking corruption. In addition, the local governments need to strengthen the coordination of cross-regional industrial development and optimize fiscal subsidies to promote ecological compensation and transfer payments. For supervising the policy implementation of local governments thoroughly, the central government needs to reduce the environmental inspection cost using the evaluation framework of cost-benefit analysis, strengthen the haze governance accountability through normalizing supervision, and broaden the public’s external supervision channels. It is vital to take effective measures to carry out haze collaborative governance, providing a useful experience for developing countries.

Changes in Air Pollution Control Policy Instruments: Based on a Textual Analysis for Southwest China 2010–2021

An important task in the construction of China’s ecological civilization, the selection and implementation of policy instruments fully reflect the actual effectiveness of the government’s efforts to control air pollution. Based on the content analysis method, this study examines the changing process of air pollution control policy instruments in southwest China from 2010 to 2021 in terms of implementation, synergy, and integration of policy instruments. The results show that, in terms of the degree of mandatory, the frequency of using policy instruments generally increased with time, but the overall balance of the instrument portfolio was poor. In terms of the degree of synergy, a gradual shift occurred from government-led to government-society governance. However, the concept and modes of inter-governmental linkage and cross-regional collaborative governance need to be improved. As for the degree of systemic, a clear trend of instrument integration and more frequent information interaction was found. Emergency-oriented characteristics appear strong, but a regular governance mechanism is lacking. Therefore, this paper provides policy suggestions and academic considerations for further improving the effectiveness of air pollution management in southwest China from three aspects: optimizing the policy tool system, deepening the regional joint prevention and control mechanism of air pollution, and promoting intelligent air pollution management.

Improved air quality from China’s clean air actions alleviates health expenditure inequality

Clean air actions aimed at improving air quality in China have brought about significant health benefits, thereby generating substantial savings in air-pollution-related healthcare spending. Yet, uneven regional air quality improvements and economic developments may alter existing inequality in health expenditures in the context of scarce healthcare resources. Here, we developed an econometric model that resolves individual characteristics at the city level to examine the disparity of public health expenditures in air quality improvements across regions differing in economic development and healthcare coverages and projected a range of future health expenditure savings under different air quality targets. We find that of the estimation on four air-pollution-related diseases (COPD, LRI, IHD, and stroke) in 98 cities over the year 2015–2017, a decline of 8.26 % in average hospitalization days and 10.21 % in hospitalization expenses was achieved, leading to a reduction of 8.09 % in total health expenditures as the implementation of clean air actions. Improved air quality has declined health expenditure inequality in low-middle cities and cities with imbalanced healthcare coverage. For example, the total expenses for the four diseases declined significantly in the low (−11.31 %) and medium (−7.34 %) per capita GDP groups, as well as a remarkable decline in the fewer medical resources. Health savings in some future scenarios are significant, showing substantial health expenditure savings under different air quality targets, but the savings will be greatly offset by an aging society. For example, In the Low-Level Improvement Pathway of air quality targets with aging (LLIPA scenario), health expenditure savings will be about 3537, 464, and 311 million CNY in the eastern, central, and western regions in 2035, respectively. Our findings thus highlight the importance of strengthening air pollution control policies and considering the equality of alleviating regional public health costs.

Impacts of Agricultural Soil NOx Emissions on O3 Over Mainland China

AbstractRegional ozone (O3) pollution has currently become an urgent air pollution problem in China. Nitrogen oxides (NOX) are key precursor pollutants for O3 formation. However, pollution control policies have generally neglected or simplified the impacts of soil NOX emissions in rural regions. This study aims to evaluate the effect of soil NOX emissions on O3 in agricultural areas of China based on high‐resolution soil NOX emission inventory and the WRF‐Chem air quality model. We first validate the simulation result of the O3 concentration with surface O3 observations over China using the selected optimal physicochemical scheme. After that, four scenarios are designed to simulate and analyze the regional sensitivity of O3 to soil NOX emissions and to quantify the effect of soil NOX emissions on O3 concentration. The results indicate that as soil NOX emissions increase, the overall spatial pattern does not change notably over mainland China, but the O3 concentration increases remarkably in the northwest and northeast of China. Additionally, we discover that changes of O3 concentration across mainland China differ substantially among regions. Our results highlight the effect of soil NOX emissions on air quality in agricultural areas. The control of O3 pollution in China should take into account the comprehensive consequences of various emission sources, as well as their regional differences.

Divergent Energy-Climate Nexus in the Global Fuel Combustion Processes.

Fuel combustion provides basic energy for the society but also produces CO2 and incomplete combustion products that threaten human survival, climate change, and global sustainability. A variety of fuels burned in different facilities expectedly have distinct impacts on climate, which remains to be quantitatively assessed. This study uses updated emission inventories and an earth system model to evaluate absolute and relative contributions in combustion emission-associated climate forcing by fuels, sectors, and regions. We showed that, from 1970 to 2014, coal burned in the energy sector and oil used in the transportation sector contributed comparable energies consumed (24 and 20% of the total) but had distinct climate forcing (1 and 40%, respectively). Globally, coal burned for energy production had negative impacts on climate forcing but positive effects in the residential sector. In many developing countries, coal combustion in the energy sector had negative radiative forcing (RF) per unit energy consumed due to insufficient controls on sulfur and scattering aerosol levels, but oils in the transportation sector had high positive RF values. These results had important implications on the energy transition and emission reduction actions in response to climate change. Distinct climate efficiencies of energies and the spatial heterogeneity implied differentiated energy utilization strategies and pollution control policies by region and sector.

Dynamic modeling to analyze the impacts of carbon reduction policies, Iran's electricity industry.

Sustainable development and reducing greenhouse gas emissions require optimizing the structure of energy production as the main factor in the emission of pollutants in Iran's electricity industry. Over the past years, environmental scientists have proposed carbon tax (CT) and carbon emission trading (CET) as policy tools to reduce pollutant emissions in developed and developing economies. Due to poor access to environmental data and the lack of effective pollution control laws and policies in Iran, efforts to realize the effectiveness of these policies in Iran have been inadequate. The present paper establishes a system dynamics model with feedback to fill these gaps in analyzing the effects of the CT and CET schemes. The following eight scenarios were considered and examined: a baseline scenario (BAU scenario), an emission tax scenario, an emission trading mechanism scenario, an energy supply scenario (to increase electricity generation), an emission tax scenario with investment in technology research and development (to increase energy efficiency), a tax scenario on emissions along with increasing energy supply, the scenario of emission trading mechanism with investment in technology research and development, and the scenario of emission trading mechanism with increasing energy supply. The results showed that while both CT and CET policy groups can be effective in reducing carbon emissions in thermal power plants, in the long run, using price tools to maintain energy security reduces the efficiency of policies greatly. Nevertheless, the effectiveness of CT policy in reducing emissions is higher. Also, despite the allocation of revenue from the application of scenarios to the development of renewable energy, the development of this sector cannot compensate for the reduction in the supply of thermal power plants following the implementation of these policies. As a result, other supportive policies are necessary for the further and faster development of the renewable sector and optimizing the energy structure with an emission reduction approach.

Trends of Full-Volatility Organic Emissions in China from 2005 to 2019 and Their Organic Aerosol Formation Potentials

Emissions of organic compounds have strong influences on the environment. Most previous emission inventories only cover the emissions of particulate organic carbon and nonmethane volatile organic compounds (NMVOC) but neglect the semivolatile and intermediate volatile organic compounds (S/IVOC), which considerably contribute to the organic aerosol (OA) burden. Herein, we developed a full-volatility emission inventory of organic compounds in China from 2005 to 2019 and analyzed the OA formation potential (OAFP) of each volatility bin and source using a two-dimensional volatility basis set (2D-VBS) box model. The emissions of low/extremely low/ultralow VOC (xLVOC) decreased substantially during 2005–2019, while the emissions of SVOC showed significant decline after 2014, mainly because of reduced residential biomass consumption. IVOC and VOC emission amounts in 2019 were similar to those in 2005; however, the major sources of emissions changed substantially. Emissions from volatile chemical products (VCP) increased significantly and became the dominant source of IVOC and VOC emissions. The S/IVOC from VCP contributed 1322 kt of OAFP in 2019, higher than the total anthropogenic xLVOC emissions. Considering the high of S/IVOC, future air pollution control policies should prioritize VCP, residential biomass burning, and diesel vehicles.

Aerosol Characterization of Northern China and Yangtze River Delta Based on Multi-Satellite Data: Spatiotemporal Variations and Policy Implications

Horizontal and vertical distributions of aerosol properties in the Taklimakan Desert (TD), North central region of China (NCR),North China Plain(NCP), and Yangtze River Delta (YRD) were investigated by statistical analysis using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) L3 data from 2007 to 2020, to identify the similarities and differences in atmospheric aerosols in different regions, and evaluate the impact of pollution control policies developed in China in 2013 on aerosol properties in the study area. The aerosol optical depth (AOD) distribution had substantial seasonal and spatial distribution characteristics. AOD had high annual averages in TD (0.38), NCP (0.49), and YRD (0.52). However, these rates showed a decline post-implementation of the long-term pollution control policies; AOD values declined by 5%, 13.8%, 15.5%, and 23.7% in TD, NCR, NCP, and YRD respectively when comparing 2014–2018 to 2007–2013, and by 7.8%, 11.5%, 16%, and 10.4% when comparing 2019–2020 to 2014–2018. The aerosol extinction coefficient showed a clear regional pattern and a tendency to decrease gradually as height increased. Dust and polluted dust were responsible for the changes in AOD and extinction coefficients between TD and NCR and NCP and YRD, respectively. In TD, with change of longitude, dust aerosol first increased and then decreased gradually, peaking in the middle. Similarly in NCP, polluted dust aerosol first increased and then decreased, with a maximum value in the middle. The elevated smoke aerosols of NCP and YRD were significantly higher than those observed in TD and NCR. The high aerosol extinction coefficient values (>0.1 km−1) were mainly distributed below 4 km, and the relatively weak aerosol extinction coefficients (>0.001 km−1) were mainly distributed between 5–8 km, indicating that the high-altitude long-range transport of TD and NCR dust aerosols affects NCP and YRD.

Analysis of the urban turbidity island effect: a case study in Beijing City, China

The urban turbidity island (UTI) effect is an important research topic in urban climate studies. It is closely related to urban visibility and the health of urban residents; however, it has received little attention in previous research. This study analyzes the temporal and spatial distribution characteristics of the UTI effect through the combined use of satellite remote sensing and ground observation data. Specifically, absolute and relative urban turbidity island intensity (UTII_A and UTII_R) indices are proposed and calculated for 2000–2020 by using aerosol data products and atmospheric fine particle mass concentration inversion products, which are represented by aerosol optical depth (AOD), PM1, PM2.5, and PM10. The results show the following: (a) there has been a clear footprint of the UTI effect in Beijing since 2000, generally consistent with trends of urban sprawl; (b) there are great differences in the interannual distribution of AOD, normalized AOD and PM values in urban and suburban areas; and (c) there are seasonal differences in the UTI distribution and air pollutant concentrations. The differences among indices between urban and suburban areas are mainly caused by heat island-induced air convection, complex structures in urban areas and regional weather conditions. Importantly, the interannual distribution of AOD and UTII_A of PM values decreased from 2014 to 2020, indicating that the government’s air pollution control policy has significantly improved air quality. Analysis from this study could support the formulation of urban planning and control policies to guide human activities.

Ambient Fine Particulate Matter Exposure and Blood Pressure: Evidence from a Large Chinese Multiple Follow-Up Study

ObjectiveThis study aimed to investigate the association of ambient PM2.5 exposure with blood pressure (BP) at the population level in China. MethodsA total of 14,080 participants who had at least two valid blood pressure records were selected from the China Health and Retirement Longitudinal Survey during 2011–2015. Their long-term PM2.5 exposure was assessed at the geographical level, on the basis of a regular 0.1° × 0.1° grid over China. A mixed-effects regression model was used to assess associations. ResultsEach decrease of 10 μg/m3 in the 1 year-mean PM2.5 concentration (FPM1Y) was associated with a decrease of 1.24 [95% confidence interval (CI): 0.84–1.64] mmHg systolic BP (SBP) and 0.50 (95% CI: 0.25–0.75) mmHg diastolic BP (DBP), respectively. A robust association was observed between the long-term decrease in PM2.5 and decreased BP in the middle-aged and older population. Using a generalized additive mixed model, we further found that SBP increased nonlinearly overall with FPM1Y but in an approximately linear range when the FPM1Y concentration was < 70 μg/m3; In contrast, DBP increased approximately linearly without a clear threshold. ConclusionEfficient control of PM2.5 air pollution may promote vascular health in China. Our study provides robust scientific support for making the related air pollution control policies.

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.

Meteorological impacts on surface ozone: A case study based on Kolmogorov–Zurbenko filtering and multiple linear regression

Ozone variation, excluding meteorological effects, is very important to assess the effects of air pollution control policies. In this study, the Kolmogorov-Zurbenko (KZ) filter method and multiple linear stepwise regression are combined to study the impact of meteorological parameters on ozone concentration over the past 5 years (2016–2020) in a petrochemical industrial city in northern China. Monte Carlo simulations were used to evaluate the reliability for the potential quasi quantitative prediction of the baseline component. The average level of the city and the details of five stations in the city were studied. The results show that the short-term, seasonal, and long-term component variances of maximum daily running average 8 h (MDA8) ozone in Zibo city (City) decomposed by the KZ filter account for 32.06%, 61.67% and 1.15% of the total variance, for a specific station, the values were 32.37%–34.90%, 56.64%–62.00%, and .35%–3.14%, respectively. The average long-term component increase rate is 3.19 μg m−3 yr−1 on average for the city, while it is 1.52–5.95 μg m−3 yr−1 for a specific station. The overall meteorological impact was not stable and fluctuated between −2.60 μg m−3 and +3.77 μg m−3. This difference in trends between the city and specific stations implied that the O3 precursor’s mitigation strategy should be more precise to improve its practical effects.

Big data analysis of water quality monitoring results from the Xiang River and an impact analysis of pollution management policies.

Water pollution prevention and control of the Xiang River has become an issue of great concern to China's central and local governments. To further analyze the effects of central and local governmental policies on water pollution prevention and control for the Xiang River, this study performs a big data analysis of 16 water quality parameters from 42 sections of the mainstream and major tributaries of the Xiang River, Hunan Province, China from 2005 to 2016. This study uses an evidential reasoning-based integrated assessment of water quality and principal component analysis, identifying the spatiotemporal changes in the primary pollutants of the Xiang River and exploring the correlations between potentially relevant factors. The analysis showed that a series of environmental protection policies implemented by Hunan Province since 2008 have had a significant and targeted impact on annual water quality pollutants in the mainstream and tributaries. In addition, regional industrial structures and management policies also have had a significant impact on regional water quality. The results showed that, when examining the changes in water quality and the effects of pollution control policies, a big data analysis of water quality monitoring results can accurately reveal the detailed relationships between management policies and water quality changes in the Xiang River. Compared with policy impact evaluation methods primarily based on econometric models, such a big data analysis has its own advantages and disadvantages, effectively complementing the traditional methods of policy impact evaluations. Policy impact evaluations based on big data analysis can further improve the level of refined management by governments and provide a more specific and targeted reference for improving water pollution management policies for the Xiang River.

To breathe or not to breathe: Implications of hazardous air quality

Climate change has affected all living beings and has become a global issue. Over the years, environmental pollution has increased drastically. Air pollution is one of the leading causes of many health issues, specifically respiratory problems. The disease spectrum, from allergies to cancers, is the cause of the economic burden that impacts the countries' welfare. Unhealthy living and ineffective pollution control policies need to be addressed. The role of the government and public health sector is to change their approach to ensure a healthy, clean environment and reduce carbon and other pollutants, thus improving the air quality for better living.

Enhancement of Photovoltaic Power Potential in China from 2010 to 2020: The Contribution of Air Pollution Control Policies

China is expected to have a total installed photovoltaic capacity of 1300 GW in 2050, accounting for 39% of the national electricity consumption. However, air pollutants consisting of gases and particulates have attenuation effects on the solar radiation reaching the photovoltaic panels. This work purports to assess the influence of air pollutants on the photovoltaic power potential. We calculated the hourly point-of-array irradiance (POAI) in China for 2010–2020 with a spatial resolution of 0.1° × 0.1° using the PV_LIB model and assessed the effect of air pollutants on POAI. The results indicated that the annual average POAI in China for 2010–2020 ranged from 118 to 286 Wm−2. The Air Pollution Control Action Plan (APPCAP) has played a certain role in photovoltaic power potential, and POAI has increased in areas where surface concentrations of air pollutants have declined. Especially in North China, the surface concentrations of CO, NO2, PM10, PM2.5, and SO2 decreased throughout the APPCAP period with −0.446, −4.985, −35.610, −30.700, and −26.251 μgm−3, respectively, corresponding to an increase in POAI of up to 4.917 Wm−2. The surface concentrations of CO, NO2, PM10, PM2.5, and SO2 were negatively correlated with POAI, with correlation coefficients of −0.764, −0.854, −0.204, −0.110, and −0.664, respectively. Surface concentrations of air pollutants (CO, NO2, PM10, PM2.5, and SO2) and clear-sky POAI in 2018 showed a High-Low clustering in Northeast China and North China. This study demonstrates the role of China’s air pollution control policy in enhancing photovoltaic power potential.

A Bidirectional Long Short-Term Neural Network Model to Predict Air Pollutant Concentrations: A Case Study of Tehran, Iran

Air pollution is a major challenge of new civilized world, which has intensified as a result of industrialization, urbanization expansion, and rapid traffic growth and increasing anthropogenic activities. Air pollutants can cause serious toxicological impacts on human health and the environment. Therefore, knowledge of pollutant concentrations can be used as key information in pollution control programs and policies. The main aim of this research was to provide a new hybrid model for short-term air quality prediction. Air pollution is the presence of pollutants in the air such as sulphur dioxide (SO2), particle substances (PM), nitrogen oxides (NOX), and ozone (O3). Sulfur dioxide (SO2), particulate matter with particle size less than 10 microns (PM10), particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) in the air all have an impact on the Air Quality Index (AQI). The AQI's variation is extremely nonlinear and complex. One of the most desired features of an AQI prediction model is the ability to predict the AQI level that is dangerous to the population. To increase forecast accuracy, an air quality prediction model based on deep recurrent neural networks and the BiLSTM model is created in this research. AQIs (O3, PM2.5, PM10, NO2, SO2, CO) prediction has been examined among several air pollutants. The data on O3, PM2.5, PM10, NO2, SO2, and CO concentrations are average daily reports from the Tarbiat Modares station in Tehran's 6th district, collected between March 2018 and June 2019. The station is located at 35.71751 latitude and 51.385909 longitudes. The proposed model was compared with Shallow Learning and MLP neural networks which had better and more reliable results over the aforementioned neural network in predicting the concentration of pollutants. The regression coefficient obtained to predict the concentrations of pollutants O3, PM2.5, PM10, NO2 and SO2 was 0.87, 0.62, 0.84, 0.67, 0.75 and 0.72, respectively. The results showed that the Bi-LSTM deep neural network model is a reliable method to predict hourly air pollutant concentrations.

Pollution characteristics and probabilistic health risk of potentially hazardous elements in soils near a typical coal mine in Panzhihua City, Southwest China

This study first assessed the pollution characteristics and probabilistic health risks of potentially hazardous elements (PHEs) in soils from the Dabaoding coal mining area in southwest China using Monte Carlo simulation. Experimental results showed that Cd was moderately enriched in soils, while Ni, Cr, and V were slightly enriched. However, the geoaccumulation index (Igeo) illustrated that the coal mining area had a low level of Cd pollution. PHEs produced a very high ecological risk to soils in the coal mining area, whereas Cd showed the highest contribution (82.56%). The mean hazard index of all soil PHEs was 7.45E - 02 and 4.18E - 01 for local adults and children, respectively, all of which were obviously lower than the maximum acceptable level of 1.0. However, Monte Carlo simulation analysis indicated that 1.08% of noncarcinogenic risk values for local children still exceeded the maximum acceptable level. Additionally, 10.84% and 18.40% of the total carcinogenic risk values for local adults and children, respectively, exceeded the threshold of 1E - 04. Indeed, Cr and Ni had the highest contributions to noncarcinogenic and carcinogenic risks, respectively. These findings suggest that Cd, Cr, and Ni should be identified as priority pollutants in coal mining areas. This study also provides valuable implications for policy-makers and environmental engineers, proposing efficient policies for better soil pollution control and remediation strategies in coal mining areas.

Can Pollution Regulations Enable Key Industries to Reduce CO2 Emissions?—Empirical Evidence from China, Based on Green Innovative Technology Patents and Energy Efficiency Perspectives

Under the influence of the dual policies of sustainable economic development and the national dual-carbon target, the establishment of an environmental protection department for the treatment of heavily polluting industries is imminent, and the country has launched pollution control policies and regulations to restrict the emission rights of heavily polluting industries. Therefore, this paper focuses on whether the restriction of emission rights in key industries has reduced carbon emissions. To achieve this, this paper uses panel data of prefecture-level cities in China from 2006 to 2019 and adopts a two-way fixed-effects DID model to systematically analyze the impact of the key pollution industry governance policies launched by the Ministry of Environmental Protection on CO2 emissions in 2017. And further analyze the role of variables such as green technology innovation patents and energy efficiency using this model, while parallel trend tests and placebo tests, and related policies are used to ensure the robustness of the regression results. This paper reveals that: (1) The heavy pollution industry governance policy implemented in 2017 can effectively reduce CO2 emissions in the cities of the treated group, and the effect is more significant in the year of policy implementation; (2) Green utility patents and energy-use efficiency are the effective mediating mechanisms to reduce CO2 emissions; (3) Over time, the effect of heavy pollution industry governance policy on CO2 emissions gradually decreases; (4) The reliability of the baseline regression results of this paper is proved by the use of parallel trend tests, placebo tests, and tests excluding the influence factors such as relevant policies in the same period. Therefore, the key polluting industries treatment policy launched by China’s Ministry of Environmental Protection in 2017 under the recent dual-carbon policy development goals formulated by China, can effectively reduce carbon emissions; however, in the future economic development process, the government should give more consideration to the continuity of the policy impact and its coherence on economic development when implementing the policy.

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.

Inequality in air pollution mortality from power generation in India

India’s coal-heavy electricity system is the world’s third largest and a major emitter of air pollution and greenhouse gas emissions. Consequently, it remains a focus of decarbonization and air pollution control policy. Considerable heterogeneity exists between states in India in terms of electricity demand, generation fuel mix, and emissions. However, no analysis has disentangled the expected, state-level spatial differences and interactions in air pollution mortality under current and future power sector policies in India. We use a reduced-complexity air quality model to evaluate annual PM2.5 mortalities associated with electricity production and consumption in each state in India. Furthermore, we test emissions control, carbon tax, and market integration policies to understand how changes in power sector operations affect ambient PM2.5 concentrations and associated mortality. We find poorer, coal-dependent states in eastern India disproportionately face the burden of PM2.5 mortality from electricity in India by importing deaths. Wealthier, high renewable energy states in western and southern India meanwhile face a lower burden by exporting deaths. This suggests that as these states have adopted more renewable generation, they have shifted their coal generation and associated PM2.5 mortality to eastern areas. We also find widespread sulfur emissions control decreases mortality by about 50%. Likewise, increasing carbon taxes in the short term reduces annual mortality by up to 9%. Market reform where generators between states pool to meet demand reduces annual mortality by up to 8%. As India looks to increase renewable energy, implement emissions control regulations, establish a carbon trading market, and move towards further power market integration, our results provide greater spatial detail for a federally structured Indian electricity system.