Air Pollution Prevention Research Articles

Advances in amelioration of air pollution using plants and associated microbes: An outlook on phytoremediation and other plant-based technologies

Globally, air pollution is an unfortunate aftermath of rapid industrialization and urbanization. Although the best strategy is to prevent air pollution, it is not always feasible. This makes it imperative to devise and implement techniques that can clean the air continuously. Plants and microbes have a natural potential to transform or degrade pollutants. Hence, strategies that use this potential of living biomass to remediate air pollution seem to be promising. The simplest future trend can be planting suitable plant-microbe species capable of removing air pollutants like SO2, CO2, CO, NOX and particulate matter (PM) along roadsides and inside the buildings. Established wastewater treatment strategies such as microbial fuel cells (MFC) and constructed wetlands (CW) can be suitably modified to ameliorate air pollution. Green architecture involving green walls and green roofs is facile and aesthetic, providing urban ecosystem services. Certain microbe-based bioreactors such as bioscrubbers and biofilters may be useful in small confined spaces. Several generative models have been developed to assist with planning and managing green spaces in urban locales. The physiological limitations of using living organisms can be circumvent by applying biotechnology and transgenics to improve their potential. This review provides a comprehensive update on not just the plants and associated microbes for the mitigation of air pollution, but also lists the technologies that are available and/or can be modified and used for air pollution control. The article also gives a detailed analysis of this topic in the form of strengths-weaknesses-opportunities-challenges (SWOC). The strategies mentioned in this review would help to attain corporate Environmental Social and Governance (ESG) and Sustainable Development Goals (SDGs), while reducing carbon footprint in the urban scenario. The review aims to emphasise that urbanization is possible while tackling air pollution using facile, green techniques involving plants and associated microbes.

Residual neural network with spatiotemporal attention integrated with temporal self-attention based on long short-term memory network for air pollutant concentration prediction

Accurate prediction of air pollutant concentrations can be effective in controlling and preventing air pollution, which is crucial for both the government's policy response and the public's reasonable ability to avoid air pollution. In this study, we propose a model based on the principle of big data correlation and deep learning techniques for predicting the concentration of air pollutant concentrations. The proposed model, named STARes-SaLSTM, consists of a residual neural network with spatiotemporal attention (STAResNet) and a temporal self-attention based on long short-term memory network (SaLSTM). The spatiotemporal attention module in STAResNet enhances the original input information by weighting it in both spatial and temporal dimensions, meanwhile the features of the spatial distribution of pollutant and meteorological information across many cities are deeply extracted using ResNet. A novel self-attention method known as temporal self-attention is developed to extract the temporal dependency of air pollutants. LSTM is used as a sequence encoder in this temporal attention method to calculate queries, keys, and values to acquire a more comprehensive temporal dependence than is possible with normal self-attention. The STARes-SaLSTM model successfully predicts air pollution concentrations in the target city for the future by extracting spatiotemporal correlation of feature sequence. In comparison to previous neural network models and conventional methods, the model increases the accuracy of pollutant concentration prediction. The suggested prediction model works well for the one to 3 h prediction job, with a root mean square error (RMSE) ranging from 6.716 to 11.648. Furthermore, even for the 1–24 h prediction assignment, we executed multi-scale prediction in the target city and obtained a reasonable performance, with an average RMSE value of 20.576.

Prediction of PM2.5 with a piecewise affine model considering spatial-temporal correlation

Over the past several decades, several air pollution prevention measures have been developed in response to the growing concern over air pollution. Using models to anticipate air pollution accurately aids in the timely prevention and management of air pollution. However, the spatial-temporal air quality aspects were not properly taken into account during the prior model construction. In this study, the distance correlation coefficient (DC) between measurements made in various monitoring stations is used to identify appropriate correlated monitoring stations. To derive spatial-temporal correlations for modeling, the causality relationship between measurements made in various monitoring stations is analyzed using Transfer Entropy (TE). This work explores the process of identifying a piecewise affine (PWA) model using a larger dataset and suggests a unique hierarchical clustering-based identification technique with model structure selection. This work improves the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) by introducing Kullback-Leibler (KL) Divergence as the dissimilarity between clusters for handling clusters with arbitrary shapes. The number of clusters is automatically determined using a cluster validity metric. The task is formulated as a sparse optimization problem, and the model structure is selected using parameter estimations. Beijing air quality data is used to demonstrate the method, and the results show that the proposed strategy may produce acceptable forecast performance.

How does the impact of a two-stage air pollution control policy on air quality different? Evidence from 258 cities in China

Air pollution control has become a core task in the construction of China's “ecological civilization”. From 2013 to 2020, the Chinese government successively implemented two consecutive environmental management policies targeting air pollution, namely the Air Pollution Prevention and Control Action Plan (APPCAP) and the Blue Sky Defense War (BSDW). To compare and analyze the effects of these two consecutive policies on air pollution control and their differences in governance mechanisms, this paper employs daily air pollution data from 258 cities in China from January 2013 to September 2020 for empirical analysis. We examines the direct impact of the two-stage policies on air pollution and explores the impact mechanisms from three aspects: technological effects, structural effects, and scale effects. The results indicate: (1) Both APPCAP and BSDW significantly reduced AQI, PM2.5, PM10, SO2, nitrogen dioxide, and CO. (2) Compared to APPCAP, BSDW had a stronger effect on reducing nitrogen dioxide and managed O3 in coordination. (3) APPCAP did not have significant impacts on technology, scale, and structure effects, whereas BSDW significantly promoted technological innovation and industrial restructuring but significantly suppressed the scale effect. Our work provides new empirical evidence for the policy heterogeneity in air pollution control and offers useful policy implications for improving air quality and coordinated pollution control.

Direct radiative forcing of light-absorbing carbonaceous aerosols in China

China is an important emitter of light-absorbing carbonaceous aerosols (LACs), including black carbon (BC) and brown carbon (BrC). Currently, there are large uncertainties in model-estimated direct radiative forcing (DRF) of LACs, partially due to the poor understanding of the emissions and optical properties of LACs. In this study, we estimated the DRF of LACs over China during the implementation of the Air Pollution Prevention and Control Action Plan (APPCAP) using the global chemical transport model (GEOS-Chem) coupled with the Rapid Radiative Transfer Model of GCMs (RRTMG). We updated the refractive index of BC, includedbiomass burning (BB) sources, biofuel (BF) and coal combustion (CC) sources in the residential sector as BrC emission sources and the optical properties were updated, which were not fully considered in the previous model studies. Our results showed that model could reasonably capture the spatial and temporal variations of LACs in China with the correlation coefficients between model simulated and Aerosol Robotic Network (AERONET) observed daily absorption aerosol optical depth (AAOD) of LACs at 440 nm above 0.63 and the corresponding values of the normalized mean bias within ±30%. The simulated annual mean LACs AAOD at 440 nm in China was 0.016 (0.021) in 2017 (2014) and BrC contributed about 20% (21%). The estimated annual mean clear-sky LACs DRF at the top of the atmosphere in China was 1.02 W m−2 in 2017 and 1.38 W m−2 in 2014, and the contribution of BrC was about 10% and 11%, respectively, which was dominated by the BF sources (46% in 2017 and 44% in 2014) and the BB sources (38% in 2017 and 43% in 2014), with CC sources being low (16% in 2017 and 13% in 2014). The annual mean AAOD and DRF of LACs in China decreased by 0.005 and 0.36 W m−2 from 2014 to 2017, which were largely attributed to the reductions of anthropogenic emissions during the implementation of APPCAP. Our results would improve the understanding of the light absorption capacity and climate effects of LACs in China.

Comparison of Air Pollutants during the Two COVID-19 Lockdown Periods in Winter 2019 and Spring 2022 in Shanghai, China

During the winter of 2019, the global outbreak of COVID-19 prompted extensive research on urban air pollution under lockdown measures. However, these studies predominantly focused on winter conditions, thereby limiting investigations into changes in urban air pollutants during other seasons that were also subject to lockdown restrictions. Shanghai, China, has undergone two COVID-19 lockdown periods in two seasons: winter 2019 and spring 2022. The seasonal variations and human activities were represented by meteorological factors and nighttime light brightness in this paper, respectively. The reduction in human-related emissions during the two lockdown periods was estimated based on the targets outlined in China’s Air Pollution Prevention and Control Action Plan. The results showed significant reductions in NO2 and PM particles during the two lockdown periods, both accompanied by a notable increase in O3 concentration. In comparison to the winter lockdown, there was an approximate 40% decrease in the NO2 and PM2.5 concentrations in the spring, while the O3 concentration exhibited an increase of 48.81%. Furthermore, due to shifting wind patterns during the two lockdowns from winter to spring, the high-pollution core areas shifted 20–25 km southeastward in the spring. The PM particles and NO2 concentrations exhibited a considerable impact from human activities, whereas the O3 concentration was affected mostly by seasonal change and interactions among air pollutants. Compared to the corresponding non-lockdown condition, the concentration of CO decreased during the winter lockdown; however, it increased during the spring lockdown. The different change in CO concentration during the two lockdown periods was found to have a lower effect on the O3 concentration than that caused by changes in meteorological factors and nitrogen oxide (NO, NO2) concentrations. In summary, the impact of COVID-19 lockdown periods on urban air pollutants was more pronounced in spring compared to winter, and the interactions among air pollutants also underwent alterations.

Marine fuel restrictions and air pollution: A study on Chinese ports considering transboundary spillovers

Docked vessels in ports can be one of the dirtiest emitters in terms of local air pollutants. This paper evaluates whether the regulation of Emission Control Areas in China reduces air pollutants emissions in port cities. The transboundary spillovers of air pollution, induced by meteorological conditions, results in air pollution concentrations at specific locations encompassing both locally generated pollutants and those originating from elsewhere. Therefore, it is challenging to discern the emission reduction effects of environmental regulatory policies through air pollutant monitoring concentrations. This paper develops an approach to isolate transboundary spillovers induced by the wind, estimate locally produced pollution, and utilize the concentrations of locally produced pollution to identify the treatment effects of the Chinese Emission Control Areas policy. The results find that the policy has significantly reduced the locally produced sulfur dioxide concentrations by 3.41% in port cities. However, the omission of transboundary spillovers could yield contrasting results, suggesting that ignoring the pollution transport could lead to erroneous conclusions. The research findings of this study hold significant policy implications, highlighting the importance of accounting for transboundary transport of air pollution in evaluating the local government’s environmental efforts and implementing supra-city environmental policies to prevent air pollution.

Impacts of transportation emissions on horizontal and vertical distributions of air pollutants in Shanghai: Insights from emission reduction in COVID-19 lockdown

Transportation is a major sector of anthropogenic emissions in urban areas and deteriorates air quality. The surface and vertical observational data were combined with the model results to reveal its impact on the horizontal and vertical variations of pollutants during the COVID-19 lockdown period. The evident reductions in ambient PM2.5 (∼30%) and NO2 (∼50%) concentrations but a ∼25% increase in O3 concentration were observed at the transportation sites. On the vertical scale, a uniform decrease of ∼28% in PM2.5 concentrations was observed within 600 m. However, the vertical profiles of NO2 and O3 exhibited increasing vertical variation rates with concentrations varying significantly within 400 m. Meanwhile, Ox shared a similar pattern of vertical profile with O3, with a uniform increase (∼5%) within 600 m in the urban area. The WRF-CMAQ model reproduced the variations, and revealed that the reduction of transportation emissions was the key factor contributing to the increase of urban O3 and Ox due to the weakened NO titration effect. The simulated vertical profile of NO2 was featured by a decreasing curve, while that of O3 exhibited the opposite trend. We find that the transportation emissions impact vertical concentrations of NO2 and O3 within at most 400 m. The process analysis revealed that the vertical transport and horizontal transport from bay areas contributed to O3 in the urban area, while chemical processes mainly consumed it. The reduction in transportation emissions weakened the consumption and resulted in O3 accumulation during rush hours and at night. The variation of planetary boundary layer height also favored the rise of urban O3 by promoting vertical transport at daytime and trapping it at night. The reduction in NOx emissions from the transportation enhanced O3 pollution, suggesting that collaborative reductions in VOCs from multiple sectors should be conducted. This study also indicated that regional collaborations in emission reductions were necessary for comprehensive air pollution prevention.

Experimental Study on the Forced Ventilation Safety during the Construction of a Large-Slope V-Shaped Tunnel

The special large-slope V-shaped structure of underwater tunnels changes the ventilation characteristics during tunnel construction, making the traditional experience limited. Therefore, it is urgent to study the influence of the special structure on the safety of the air environment during construction. In this paper, a series of small-scale experiments were conducted to investigate the ventilation characteristics of V-shaped tunnels. The coupled effects of ventilation parameters (distance of duct outlet from working face Lo, air velocity at the duct outlet uo) and structural characteristics (digging length Ld, slope of the uphill section q) were considered. The extreme slope of the V-shaped tunnel of 8% was considered. The flow field and pollutant transport law were determined by using CO as a tracer in the experiments. The results show that uo has a positive impact on the air return velocity, while Ld has a negative impact, and neither of the other two factors has a significant effect. The transport characteristics of CO in V-shaped tunnels differ from those in flat tunnels, with the former tending to cause unconventional areas of high pollutant concentrations in the horizontal sections. Furthermore, the correlations between CO concentration and distance, ventilation time, and the influence factors discussed in this paper are derived from the experimental results. The conclusions provide guidance for the construction of V-shaped tunnels to prevent air pollution in the construction environment and to improve the working conditions of laborers. Additionally, it can also enrich the ventilation experience in tunnel construction.

Accountability analysis of health benefits related to National Action Plan on Air Pollution Prevention and Control in China.

China is one of the largest producers and consumers of coal in the world. The National Action Plan on Air Pollution Prevention and Control in China (2013-2017) particularly aimed to reduce emissions from coal combustion. Here, we show whether the acute health effects of PM2.5 changed from 2013 to 2018 and factors that might account for any observed changes in the Beijing-Tianjin-Hebei (BTH) and the surrounding areas where there were major reductions in PM2.5 concentrations. We used a two-stage analysis strategy, with a quasi-Poisson regression model and a random effects meta-analysis, to assess the effects of PM2.5 on mortality in the 47 counties of BTH. We found that the mean daily PM2.5 levels and the SO42- component ratio dramatically decreased in the study period, which was likely related to the control of coal emissions. Subsequently, the acute effects of PM2.5 were significantly decreased for total and circulatory mortality. A 10 μg/m3 increase in PM2.5 concentrations was associated with a 0.16% (95% CI: 0.08, 0.24%) and 0.02% (95% CI: -0.09, 0.13%) increase in mortality from 2013 to 2015 and from 2016 to 2018, respectively. The changes in air pollution sources or PM2.5 components appeared to have played a core role in reducing the health effects. The air pollution control measures implemented recently targeting coal emissions taken in China may have resulted in significant health benefits.

Causal association between air pollution and autoimmune diseases: a two-sample Mendelian randomization study.

In recent years, an increasing number of observational studies have reported the impact of air pollution on autoimmune diseases (ADs). However, no Mendelian randomization (MR) studies have been conducted to investigate the causal relationships. To enhance our understanding of causality, we examined the causal relationships between particulate matter (PM) and nitrogen oxides (NOx) and ADs. We utilized genome-wide association study (GWAS) data on PM and NOx from the UK Biobank in European and East Asian populations. We also extracted integrated GWAS data from the Finnish consortium and the Japanese Biobank for two-sample MR analysis. We employed inverse variance weighted (IVW) analysis to assess the causal relationship between PM and NOx exposure and ADs. Additionally, we conducted supplementary analyses using four methods, including IVW (fixed effects), weighted median, weighted mode, and simple mode, to further investigate this relationship. In the European population, the results of MR analysis suggested a statistically significant association between PM2.5 and psoriasis only (OR = 3.86; 95% CI: 1.89-7.88; PIVW < 0.00625), while a potential association exists between PM2.5-10 and vitiligo (OR = 7.42; 95% CI: 1.02-53.94; PIVW < 0.05), as well as between PM2.5 and systemic lupus erythematosus (OR = 68.17; 95% CI: 2.17-2.1e+03; PIVW < 0.05). In East Asian populations, no causal relationship was found between air pollutants and the risk of systemic lupus erythematosus and rheumatoid arthritis (PIVW > 0.025). There was no pleiotropy in the results. Our results suggest a causal association between PM2.5 and psoriasis in European populations. With the help of air pollution prevention and control, the harmful progression of psoriasis may be slowed.

An Investigation on the use of air quality models in ship emission forecasts

Today's world trade operates on the basis of maritime transportation. Combating the environmental impacts of maritime transportation has become a global concern. MARPOL Annex VI contains rules for the prevention of air pollution from ships. Air pollution represents a more abstract pollution than other annexes of MARPOL. In this context, the first and most critical step is to measure air pollution. Nowadays it is possible to calculate ship emissions with individual ship activity data instead of the traditional, fuel-based approach. One of the most ideal data sources for this calculation is AIS data. The critical question to be answered in the literature is how to obtain the highest resolution output using AIS data. Here air quality modelling gains strategic importance. In this study, air quality models used in ship emission calculations were examined with the PRISMA method and the most commonly used Eulerian and Lagrangian models were discussed. For this purpose, studies using AIS data in ship emission calculations were separated through the Scopus database and air quality modelling studies were filtered. Thus, the profile of air quality models used in ship-borne air pollution studies in the literature was obtained and a methodological reference source was created for future studies.

Dynamic evaluation of China's atmospheric environmental pressure from 2008 to 2017: Trends and drivers

Evaluating the pressure of atmospheric pollutant emissions on the atmospheric environment is crucial for effective pollution control and emission reduction policies. This study introduces a novel Atmospheric Environmental Pressure Index (AEPI) and employs a dynamic comprehensive method to evaluate China's Atmospheric Environmental Pressure (AEP) across 31 provinces from 2008 to 2017. The drivers of AEP were analyzed using a spatial panel data model, uncovering the integral role of pollution reduction policies, particularly the Air Pollution Prevention and Control Action Plan, which led to a 25% reduction in AEP during its enforcement. Our findings reveal significant spatial disparities in AEP, with higher levels in the Beijing-Tianjin-Hebei and Yangtze River Delta regions. The regression analysis identifies economic development, industrial structure, energy efficiency, environmental regulations, and urbanization as key influencing factors, though their impacts vary across different regions, suggesting the need for region-specific pollution control policies. Furthermore, the shift in the AEP gravity center from 2008 to 2017 indicated a southeastward movement, suggesting the necessity to focus air pollution control efforts on the southeast provinces. In conclusion, the AEPI developed in this study enables comparative analysis of AEP across different regions and facilitates the monitoring of long-term trends, which is valuable in guiding regional air pollution control in China.

CO2 synergistic emission reduction and health benefits of PM2.5 reaching WHO-III level in Pearl River Delta

Protecting human health is one of the fundamental goals of continuous air quality improvement. Considering the same sources of CO2 and air pollutants emissions, their emission control measures often have a certain degree of synergistic emission reduction effects To balance the costs and policy efficiency, policy makers should assess the synergistic benefits of CO2 emission reduction when formulating air quality improvement strategies. Aimed at the air quality improvement strategy targeting WHO-III level (i.e. 15 μg/m3) for PM2.5 in the Pearl River Delta (PRD) region, this study evaluates the synergistic CO2 reduction effects and the associated health benefits from this strategy which includes a range of air pollution prevention and control measures. The results of the study show that promoting the attainment of WHO-III for PM2.5 in the PRD region can bring about 74 Mt of synergistic CO2 emission reduction, resulting in an 18% reduction of CO2 emissions in the PRD compared to 2017. Among various measures considered, industrial restructuring, power supply transition and industrial energy consumption transformation exhibit the most pronounced synergistic effects. Therefore, these measures are recommended to be prioritized and promoted in the next stage of air pollution prevention and CO2 emission reduction. Furthermore, when the PM2.5 concentration in the PRD region reaches the WHO-III level, the number of PM2.5-related deaths will be estimated to reduce approximately 5.5 thousand compared to that in the current policy scenario. Through continuous structural transformation and emission reduction efforts, it not only facilitates the decline in regional PM2.5 concentration but also helps more regional residents to live in an environment with a relatively low PM2.5 concentration. In addition, in order to reduce the health impacts of PM2.5, it is recommended that the government should guide people to change their production and living styles in order to reduce pollutant emissions from anthropogenic activities.

Sensitivities of ozone to its precursors during heavy ozone pollution events in the Yangtze River Delta using the adjoint method

Although the concentrations of five basic ambient air pollutants in the Yangtze River Delta (YRD) have been reduced since the implementation of the “Air Pollution Prevention and Control Action Plan” in 2013, the ozone concentrations still increase. In order to explore the causes of ozone pollution in YRD, we use the GEOS–Chem and its adjoint model to study the sensitivities of ozone to its precursor emissions from different source regions and emission sectors during heavy ozone pollution events under typical circulation patterns. The Multi-resolution Emission Inventory for China (MEIC) of Tsinghua University and 0.25° × 0.3125° nested grids are adopted in the model. By using the T–mode principal component analysis (T–PCA), the circulation patterns of heavy ozone pollution days (observed MDA8 O3 concentrations ≥160 μg m−3) in Nanjing located in the center area of YRD from 2013 to 2019 are divided into four types, with the main features of Siberian Low, Lake Balkhash High, Northeast China Low, Yellow Sea High, and southeast wind at the surface. The adjoint results show that the contributions of emissions emitted from Jiangsu and Zhejiang are the largest to heavy ozone pollution in Nanjing. The 10 % reduction of anthropogenic NOx and NMVOCs emissions in Jiangsu, Zhejiang and Shanghai could reduce the ozone concentrations in Nanjing by up to 3.40 μg m−3 and 0.96 μg m−3, respectively. However, the reduction of local NMVOCs emissions has little effect on ozone concentrations in Nanjing, and the reduction of local NOx emissions would even increase ozone pollution. For different emissions sectors, industry emissions account for 31 %–74 % of ozone pollution in Nanjing, followed by transportation emissions (18 %–49 %). This study could provide the scientific basis for forecasting ozone pollution events and formulating accurate strategies of emission reduction.

Assessment of CO2 Co-benefits of Air Pollution Control Policies in Taiyuan's 14th Five-Year Plan

To quantitatively evaluate the co-benefits of air pollution reduction and carbon dioxide reduction of Taiyuan's 14th Five-Year Plan air pollution prevention and control policies, this study used the Beijing-Tianjin-Hebei Greenhouse Gas-Air Pollution Interaction and Synergy Model (GAINS-JJJ) to simulate and evaluate the emission reduction potential and CO2 co-benefit of 13 air pollution control measures. The emission reductions of PM2.5, PM10, SO2, NOx, VOCs, and NH3 in 2025 were 1.8 (5%, compared with that in the baseline scenario), 2.5 (2%), 3.7 (16%), 20.0 (27%), 13.6 (15%), and 0.0 kt (0%), respectively. The reduction in CO2 emissions was 9.0 Mt (13%), whereas CH4 emissions increased by 203.3 kt (25% increase relative to that in the baseline scenario). SO2, NOx, and VOCs emission reductions derived from the power, industrial combustion, and solvent use sectors. CO2 reduction occurred mainly in the industrial combustion sector, and CH4 emission increased mainly due to the increase in coal mining activity. The highest synergistic CO2 reductions were achieved by restricting energy consumption in the high energy-consuming and high-emitting sectors; prohibiting new capacity in the steel, coke, cement, and flat glass industries; and replacing coal-fired power generation with renewable energy. Furthermore, the CO2 reduction co-benefit was highest for VOCs. In addition, this study suggests that promoting the policy of terminal electrification and simultaneously increasing the share of clean energy and the ability to consume renewable energy generation in the power sector are the keys to decreasing the emissions in Taiyuan.

Chemical composition, sources and formation mechanism of urban PM2.5 in Southwest China: a case study at the beginning of 2023

Abstract. Despite significant improvements in air quality in recent years, the Sichuan Basin (SCB) is still facing frequent haze pollution in winter, and the causes of severe haze formation have not yet been fully investigated. In this study, the chemical components of PM2.5 (i.e., particulate matter with an aerodynamic diameter of less than 2.5 µm) in a typical pollution period at the beginning of 2023 in Chengdu, a megacity in the SCB, were characterized by bulk-chemical and single-particle analysis, and the PM2.5 sources and formation mechanism of pollution were analyzed. The average mass concentration of PM2.5 during the study period was 95.6 ± 28.7 µg m−3. Organic matter (OM) was the most abundant component (35.3 %), followed by nitrate (22.0 %), sulfate (9.2 %) and ammonium (7.8 %). The individual aerosol particles were classified into five categories, i.e., mineral, OM, secondary inorganic aerosol (SIA), soot and fly ash/metal particles, and most of them were in the state of being internally mixed. The entire observation period could be divided into two non-pollution periods (NP-1 and NP-2) and two haze periods (Haze-1 and Haze-2). With the evolution of pollution, the bulk-chemical and single-particle analysis exhibited similar characteristics, suggesting that Haze-1 was mainly caused by pollutants related to fossil fuel combustion, especially local mobile sources, while Haze-2 was triggered by the rapidly increasing secondary pollutants, which mainly came from regional transmission. The PM2.5 sources included dust (8.5 %), biomass burning (3.5 %), coal combustion (15.4 %), industrial processes (6.5 %), vehicular emissions (25.6 %) and secondary sources (40.5 %). Analysis of Weather Research and Forecasting model with Chemistry (WRF-Chem) model results showed that the average contributions of local sources and regional transmission to pollution in Chengdu were the same (50 % vs. 50 %). In addition, the source composition and WRF-Chem simulation results in different periods confirmed our analysis of the formation mechanisms of the two haze events. This study confirms that further significant reductions in PM2.5 in Chengdu are still needed, and more effective policies for local emission reduction or joint prevention and control of regional air pollution will be necessary in the future.

Impact of climate change on environmental health: Challenges and opportunities to improve quality of life

Global climate change has become an urgent and complex issue in this modern era. In addition to its broad impact on the environment, climate change also has significant implications for human health. This article describes the relationship between climate change and environmental health, and identifies challenges and opportunities in addressing its impacts. Research shows that climate change is contributing to increasing global temperatures, extreme weather patterns, and overall environmental change. These impacts can affect air, water and soil quality, and increase the risk of communicable diseases, allergies, respiratory diseases and other chronic diseases. In addition, climate change can also affect food availability, food security, and the distribution of disease vectors such as mosquitoes that carry viruses. This presents new challenges in maintaining public health and dealing with threats related to climate change. However, amidst these challenges, there are also opportunities to improve quality of life and reduce the negative impacts of climate change on environmental health. Implementation of mitigation and adaptation policies that focus on reducing greenhouse gas emissions, developing renewable energy, preventing air pollution, sustainable water management, and disease monitoring and control, are some of the steps that can be taken to protect public health. In addition, public education and awareness about the relationship between climate change and environmental health also needs to be increased. With a better understanding of these impacts, society can engage in collective action to reduce greenhouse gas emissions, adopt sustainable practices, and protect environmental health.

Comparison of Different Impact Factors and Spatial Scales in PM2.5 Variation

PM2.5 particles with an aerodynamic diameter of less than 2.5 μm are receiving increasing attention in China. Understanding how complex factors affect PM2.5 particles is crucial for the prevention of air pollution. This study investigated the influence of meteorological factors and land use on the dynamics of PM2.5 concentrations in four urban agglomerations of China at different scales from 2010 to 2020, using the Durbin spatial domain model (SDM) at five different grid scales. The results showed that the average annual PM2.5 concentration in four core urban agglomerations in China generally had a downward trend, and the meteorological factors and land use types were closely related to the PM2.5 concentration. The impact of temperature on PM2.5 changed significantly with an increase in grid scale, while other factors did not lead to obvious changes. The direct and spillover effects of different factors on PM2.5 in inland and coastal urban agglomerations were not entirely consistent. The influence of wind speed on coastal urban clusters (the Pearl River urban agglomeration (PRD) and Yangtze River urban agglomeration (YRD)) was not significant among the meteorological factors, but it had a significant impact on inland urban clusters (the Beijing–Tianjin–Hebei urban agglomeration (BTH) and Chengdu–Chongqing urban agglomeration (CC)). The direct effect of land use type factors showed an obvious U-shaped change with an increase in the research scale in the YRD, and the direct effect of land use type factors was almost twice as large as the spillover effect. Among land use type factors, human factors (impermeable surfaces) were found to have a greater impact in inland urban agglomerations, while natural factors (forests) had a greater impact in coastal urban agglomerations. Therefore, targeted policies to alleviate PM2.5 should be formulated in inland and coastal urban agglomerations, combined with local climate measures such as artificial precipitation, and urban land planning should be carried out under the consideration of known impacts.

OMI-based emission source classification in East China and its spatial redistribution in view of pollution control measures

This study aims to generate a satellite-based qualitative emission source characterization for the heavily polluted eastern part of China in the 2010–2016 time period. The applied source identification technique relies on satellite-based NOx and SO2 emission estimates by OMI, their SO2:NOx ratio, and the MIX anthropogenic emission inventory to distinguish emissions from different emission categories (urban, industrial, natural) and characterize the dominant source per 0.25° × 0.25° grid cell in East China. Overall, we find good agreement between the satellite- and emission inventory–based spatiotemporal distribution and characterization of the dominant emission sources in East China in 2010–2016. In 2010, the satellite measurements suggest an emission distribution less dominated by industrial areas, a somewhat larger role for urban/transportation areas and agricultural activities, and more natural emissions in the southern part compared to the bottom-up emission categorization. In 2016, more than half of the classified emission categories over East China have remained the same. At the same time, there is a notable increase of agricultural lands and decrease of areas dominated by industry/transportation in 2016, suggestive of an overall decrease in heavy air pollution in East China over the course of 7 years. This is likely attributed to the sustained efforts of the Chinese government to drastically improve the air quality, especially since 2013 when the National Air Pollution Prevention and Control Action Plan was enacted. However, signs of urban expansion (urbanization) and rural–urban migration (“Go West” motion) stemmed from China’s rapid economic growth and labour demand are evident; escalating industrialization (even with cleaner means) and the urban population growth in East China resulted in stronger emissions from sources representing consumption and transportation which are strongly related to NO2 and PM10 pollution (rather than SO2) and are directly influenced by the population size. This resulted to a shift of the emissions from the east mainly to the north and northwest of East China. Overall, although the effectiveness of the Chinese environmental control policies has been successful, the air pollution problem remains an important concern.