Detecting the causality influence of individual meteorological factors on local PM2.5 concentration in the Jing-Jin-Ji region

Causal effects of key air pollutants and meteorology on ischemic stroke onset: A convergent cross-mapping approach

BACKGROUND

Evidence suggests that environmental factors may influence the risk of ischemic stroke(IS).1 Nevertheless, the majority of existing research has concentrated on correlation analysis, with only a limited number of studies employing specific methodologies to investigate the causal dynamics of this relationship with external drivers.

METHOD

In this study, we employed an approach known as convergent cross-mapping to identify and elucidate the causal effects of significant air pollutants and meteorological factors on the pathogenesis of IS. The city of Shouguang in the Shandong Peninsula region was selected for this study, primarily because of the environmental characteristics of the region and the notable prevalence of cases during the study period.

RESULTS

Key air pollutants and several meteorological factors in the region have a causal effects on IS. A general trend can be drawn. SO22 (ρ = 0.215, ∂=0.016), PM2.53 (ρ = 0.077, ∂=0.002), and PM104 (ρ = 0.058, ∂=0.0014) had a positive causal effects on IS,and relative humidity (ρ = 0.050, ∂=-0.009) tended to reduce the number of IS cases.

CONCLUSION

Through this case study, a causal network was developed with the aim of integrating the study of the interactions between variables and providing a clear model to aid the management of IS.

Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost sensors is particularly valuable. However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. To address this, we propose a causal feature selection approach based on convergent cross mapping within the machine learning pipeline to build more robustly calibrated sensor networks. This approach is applied in the calibration of a low-cost optical particle counter OPC-N3, effectively reproducing the measurements of PM1 and PM2.5 as recorded by research-grade spectrometers. We evaluated the predictive performance and generalizability of these causally optimized models, observing improvements in both while reducing the number of input features, thus adhering to the Occam's razor principle. For the PM1 calibration model, the proposed feature selection reduced the mean squared error on the test set by 43.2% compared to the model with all input features, while the SHAP value-based selection only achieved a reduction of 29.6%. Similarly, for the PM2.5 model, the proposed feature selection led to a 33.2% reduction in the mean squared error, outperforming the 30.2% reduction achieved by the SHAP value-based selection. By integrating sensors with advanced machine learning techniques, this approach advances urban air quality monitoring, fostering a deeper scientific understanding of microenvironments. Beyond the current test cases, this feature selection method holds potential for broader applications in other environmental monitoring applications, contributing to the development of interpretable and robust environmental models.

Real-time, single-particle chemical composition, volatility and mixing state measurements of urban aerosol particles in southwest China

To investigate the volatility of atmospheric particulates and the evolution of other particulate properties (chemical composition, particle size distribution and mixing state) with temperature, a thermodenuder coupled with a single particle aerosol mass spectrometer was used to conduct continuous observations of atmospheric fine particles in Chengdu, southwest China. Because of their complex sources and secondary reaction processes, the average mass spectra of single particles contained a variety of chemical components (including organic, inorganic and metal species). When the temperature rose from room temperature to 280°C, the relative areas of volatile and semi-volatile components decreased, while the relative areas of less or non-volatile components increased. Most (> 80%) nitrate and sulfate existed in the form of NH4NO3 and (NH4)2SO4, and their volatilization temperatures were 50-100°C and 150-280°C, respectively. The contribution of biomass burning (BB) and vehicle emission (VE) particles increased significantly at 280°C, which emphasized the important role of regional biomass burning and local motor vehicle emissions to the core of particles. With the increase in temperature, the particle size of the particles coated with volatile or semi-volatile components was reduced, and their mixing with secondary inorganic components was significantly weakened. The formation of K-nitrate (KNO3) and K-sulfate (KSO4) particles was dominated by liquid-phase processes and photochemical reactions, respectively. Reducing KNO3 and BB particles is the key to improving visibility. These new results are helpful towards better understanding the initial sources, pollution formation mechanisms and climatic effects of fine particulate matter in this megacity in southwest China.

Trace elements and microbial community composition associated with airborne PM2.5 in wetlands: A case study in Tablas de Daimiel National Park

Tablas de Daimiel National Park (TDNP) is one of the most important wetlands in the Iberian Peninsula. Due to its location near various cities and new industries focused on agricultural waste revalorization, we investigated concurrently the concentrations of particulate matter 2.5 (PM2.5) mass, trace element composition, and associated microbial communities (bacteria and fungi) during a year-long study. The goal of this study was to explore the dependencies among these physicochemical and microbiological parameters on a seasonal time scale. Additionally, we assessed meteorological conditions and back trajectories to shed light on atmospheric mechanisms and sources related to these elements. We found the variability of PM2.5 to be influenced by local meteorological parameters. Through the analysis of crustal enrichment factors (EFs), bivariate correlations, and air mass patterns, we determined that soil resuspension was the primary contributor to elevated metal concentrations in PM2.5 within the park, followed by other minor sources, such as traffic emissions and Sahara dust intrusions. The measured metal levels were used to calculate the ecological risk in the area, resulting in a low ecological risk index (RI) of 52. Shifts in microbial community structure were observed to be mainly driven by changes in air temperature and Cu concentration. The results from this study contribute to a better understanding of the environmental dynamics in TDNP. Taken together, our findings will aid in the development of effective strategies for its conservation and management.

Urban and suburban decadal variations in air pollution of Beijing and its meteorological drivers

Air pollution is a major threat to human health and ecosystems. Using 10-year (2013-2022) multi-source observations for the Beijing, China, we showed that clean-air actions have significantly reduced PM2.5, PM10, CO, NO2, and SO2 pollution, with an increase in the surface maximum daily 8-h average ozone (MDA8O3) concentrations during autumn and winter, leading to a rapid diminishment of the urban-suburban gap in air pollution. Secondary sources and vehicle emissions were enhanced in both urban and suburban areas in all seasons except summer from 2013 to 2022. By combining statistical analysis with the convergent cross-mapping model, the varying relationships between air pollution and meteorological conditions in the urban and suburban areas were delineated. The results suggested that boundary layer height and relative humidity exerted strong and stable influences on all air pollutants, except for MDA8O3, whose key meteorological driver was temperature. This study showed that increasing O3 trends in autumn and winter and aggravated O3 formation in summer in urban areas in Beijing became non-negligible from 2013 to 2022, despite the declining levels of air pollutants. Meteorological observations suggested that weather patterns in Beijing, characterized by higher temperatures, sunshine hours, and boundary layer height and lower relative humidity, have become more favorable for O3 formation in autumn and winter. Future mitigation efforts should focus on reducing VOC and NOx emissions to avoid further deterioration of O3 pollution under the frequent adverse meteorological conditions predicted under the background of global warming.

Chronic and acute health effects of PM2.5 exposure and the basis of pollution control targets

Ho Chi Minh City (HCMC) is changing and expanding quickly, leading to environmental consequences that seriously threaten human health. PM2.5 pollution is one of the main causes of premature death. In this context, studies have evaluated strategies to control and reduce air pollution; such pollution-control measures need to be economically justified. The objective of this study was to assess the socio-economic damage caused by exposure to the current pollution scenario, taking 2019 as the base year. A methodology for calculating and evaluating the economic and environmental benefits of air pollution reduction was implemented. This study aimed to simultaneously evaluate the impacts of both short-term (acute) and long-term (chronic) PM2.5 pollution exposure on human health, providing a comprehensive overview of economic losses attributable to such pollution. Spatial partitioning (inner-city and suburban) on health risks of PM2.5 and detailed construction of health impact maps by age group and sex on a spatial resolution grid (3.0 km × 3.0 km) was performed. The calculation results show that the economic loss from premature deaths due to short-term exposure (approximately 38.86 trillion VND) is higher than that from long-term exposure (approximately 14.89 trillion VND). As the government of HCMC has been developing control and mitigation solutions for the Air Quality Action Plan towards short- and medium-term goals in 2030, focusing mainly on PM2.5, the results of this study will help policymakers develop a roadmap to reduce the impact of PM2.5 during 2025-2030.

Variability of PM10 level with gaseous pollutants and meteorological parameters during episodic haze event in Malaysia: Domestic or solely transboundary factor?

Haze has become a seasonal phenomenon affecting Southeast Asia, including Malaysia, and has occurred almost every year within the last few decades. Air pollutants, specifically particulate matter, have drawn a lot of attention due to their adverse impact on human health. In this study, the spatial and temporal variability of the PM₁₀ concentration at Kelang, Melaka, Pasir Gudang, and Petaling Jaya during historic haze events were analysed. An hourly dataset consisting of PM₁₀, gaseous pollutants and weather parameters were obtained from Department of Environment Malaysia. The mean PM₁₀ concentrations exceeded the stipulated Recommended Malaysia Ambient Air Quality Guideline for the yearly average of 150 μg/m³ except for Pasir Gudang in 1997 and 2005, and Petaling Jaya in 2013. The PM₁₀ concentrations exhibit greater variability in the southwest monsoon and inter-monsoon periods at the studied year. The air masses are found to be originating from the region of Sumatra during the haze episodes. Strong to moderate correlation of PM₁₀ concentrations was found between CO during the years that recorded episodic haze, meanwhile, the relationship of PM₁₀ level with SO₂ was found to be significant in 2013 with significant negatively correlated relative humidity. Weak correlation of PM₁₀-NOx was measured in all study areas probably due to less contribution of domestic anthropogenic sources towards haze events in Malaysia.

Quantifying the Spatiotemporal Heterogeneity of PM2.5 Pollution and Its Determinants in 273 Cities in China

Fine particulate matter (PM_2.5) pollution brings great negative impacts to human health and social development. From the perspective of heterogeneity and the combination of national and urban analysis, this study aims to investigate the variation patterns of PM_2.5 pollution and its determinants, using geographically and temporally weighted regression (GTWR) in 273 Chinese cities from 2015 to 2019. A comprehensive analytical framework was established, composed of 14 determinants from multi-dimensions, including population, economic development, technology, and natural conditions. The results indicated that: (1) PM_2.5 pollution was most severe in winter and the least severe in summer, while the monthly, daily, and hourly variations showed "U"-shaped, pulse-shaped and "W"-shaped patterns; (2) Coastal cities in southeast China have better air quality than other cities, and the interaction between determinants enhanced the spatial disequilibrium of PM_2.5 pollution; (3) The determinants showed significant heterogeneity on PM_2.5 pollution-specifically, population density, trade openness, the secondary industry, and invention patents exhibited the strongest positive impacts on PM_2.5 pollution in the North China Plain. Relative humidity, precipitation and per capita GDP were more effective in improving atmospheric quality in cities with serious PM_2.5 pollution. Altitude and the proportion of built-up areas showed strong effects in western China. These findings will be conductive to formulating targeted and differentiated prevention strategies for regional air pollution control.

A Suitable Model for Spatiotemporal Particulate Matter Concentration Prediction in Rural and Urban Landscapes, Thailand

Spatiotemporal particulate matter (PM) concentration prediction using MODIS AOD with significant PM factors in rural and urban landscapes in Thailand is necessary for public health and has been complicated by the limitations of PM monitoring stations. The research objectives were (1) to identify significant factors affecting PM10 concentrations in rural landscapes and PM2.5 in urban landscapes; (2) to predict spatiotemporal PM10 and PM2.5 concentrations using geographically weighted regression (GWR) and mixed-effect model (MEM), and (3) to evaluate a suitable spatiotemporal model for PM10 and PM2.5 concentration prediction and validation. The research methodology consisted of four stages: data collection and preparation, the identification of significant spatiotemporal factors affecting PM concentrations, the prediction of spatiotemporal PM concentrations, and a suitable spatiotemporal model for PM concentration prediction and validation. As a result, the predicted PM10 concentrations using the GWR model varied from 50.53 to 85.79 µg/m3 and from 36.92 to 51.32 µg/m3 in winter and summer, while the predicted PM10 concentrations using the MEM model varied from 50.68 to 84.59 µg/m3 and from 37.08 to 50.81 µg/m3 in both seasons. Likewise, the PM2.5 concentration prediction using the GWR model varied from 25.33 to 44.37 µg/m3 and from 16.69 to 24.04 µg/m3 in winter and summer, and the PM2.5 concentration prediction using the MEM model varied from 25.45 to 44.36 µg/m3 and from 16.68 and 23.75 µg/m3 during the two seasons. Meanwhile, according to Thailand and U.S. EPA standards, the monthly air quality index (AQI) classifications of the GWR and MEM were similar. Nevertheless, the derived average corrected Akaike Information Criterion (AICc) values of the GWR model for PM10 and PM2.5 predictions during both seasons were lower than that of the MEM model. Therefore, the GWR model was chosen as a suitable model for spatiotemporal PM10 and PM2.5 concentration predictions. Furthermore, the result of spatial correlation analysis for GWR model validation based on a new dataset provided average correlation coefficient values for PM10 and PM2.5 concentration predictions with a higher than the expected value of 0.5. Subsequently, the GWR model with significant monthly and seasonal factors could predict spatiotemporal PM 10 and PM2.5 concentrations in rural and urban landscapes in Thailand.

Impact of Inter-Annual Variation in Meteorology from 2010 to 2019 on the Inter-City Transport of PM2.5 in the Beijing–Tianjin–Hebei Region

Air pollution has become a great challenge to achieving sustainable development. Among the pollutants, aerosols significantly affect human health and play an important role in global climate change. The concentration of aerosols in the ambient air is influenced strongly by the regional transport of pollutants and their precursors and may vary considerably under different meteorological conditions in different years. This inter-annual variation in meteorology may yield conflicting results in the quantification of the contribution from regional transport of air pollutants. It creates uncertainty for local governments to develop pollution control measures to reduce the challenges to sustainable development. Previous studies on this issue are often year-specific or cover short time spans, and the inter-city transport of air pollutants in the long term is still not fully understood. Therefore, in this study, the Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality (CMAQ) model was used to assess inter-annual variations in the contribution of inter-city transport to the PM2.5 concentration in the Beijing–Tianjin–Hebei region from 2010 to 2019. To highlight the impact of inter-annual variations in meteorology, the authors used the same emission inventory and the same model configurations for the 10-year simulation. The major findings can be summarized as follows: (1) Both PM2.5 concentration and inter-city transport in the Beijing–Tianjin–Hebei (BTH) region were influenced by the inter-annual variation in meteorological conditions. (2) The simulated annual average concentrations in 13 cities in BTH are highly variable, with fluctuations ranging from 30.8% to 54.1%, and more evident variations were found in seasonal results. (3) Seven out of thirteen cities have a contribution from regional transport exceeding 50%, which are located in the eastern half of the Beijing–Tianjin–Hebei region. (4) The magnitude of the regional transport contribution varies significantly among the cities of BTH, on an annual basis, from a minimum inter-annual fluctuation of 8.9% to a maximum of 37.2%, and seasonal fluctuation is even more strongly evident. These results indicate that, when formulating pollution control strategies, inter-annual changes in meteorological conditions should not be ignored.

Projected Aerosol Changes Driven by Emissions and Climate Change Using a Machine Learning Method

Projection of future aerosols and understanding the driver of the aerosol changes are of great importance in improving the atmospheric environment and climate change mitigation. The latest Coupled Model Intercomparison Project Phase 6 (CMIP6) provides various climate projections but limited aerosol output. In this study, future near-surface aerosol concentrations from 2015 to 2100 are predicted based on a machine learning method. The machine learning model is trained with global atmospheric chemistry model results and projects aerosols with CMIP6 multi-model simulations, creatively estimating future aerosols with all important species considered. PM_2.5 (particulate matter less than 2.5 μm in diameter) concentrations in 2095 (2091-2100 mean) are projected to decrease by 40% in East Asia, 20-35% in South Asia, and 15-25% in Europe and North America, compared to those in 2020 (2015-2024 mean), under low-emission scenarios (SSP1-2.6 and SSP2-4.5), which are mainly due to the presumed emission reductions. Driven by the climate change alone, PM_2.5 concentrations would increase by 10-25% in northern China and western U.S. and decrease by 0-25% in southern China, South Asia, and Europe under the high forcing scenario (SSP5-8.5). A warmer climate exerts a stronger modulation on global aerosols. Climate-driven global future aerosol changes are found to be comparable to those contributed by changes in anthropogenic emissions over many regions of the world in high forcing scenarios, highlighting the importance of climate change in regulating future air quality.

Two-stage deep learning hybrid framework based on multi-factor multi-scale and intelligent optimization for air pollutant prediction and early warning

Effective prediction of air pollution concentrations is of great importance to both the physical and mental health of citizens and urban pollution control. As one of the main components of air pollutants, accurate prediction of PM_2.5 can provide a reference for air pollution control and pollution warning. This study proposes an air pollutant prediction and early warning framework, which innovatively combines feature extraction techniques, feature selection methods and intelligent optimization algorithms. First, the PM_2.5 sequence is decomposed into several subsequences using the complete ensemble empirical mode decomposition with adaptive noise, and then the new components of the subsequences with different complexity are reconstructed using fuzzy entropy. Then, the Max-Relevance and Min-Redundancy method is used to select the influencing factors of the different reconstructed components. Then, a two-stage deep learning hybrid framework is constructed to model the prediction and nonlinear integration of the reconstructed components using a long short-term memory artificial neural network optimized by the gray wolf optimization algorithm. Finally, based on the proposed hybrid prediction framework, effective prediction and early warning of air pollutants are achieved. In an empirical study in three cities in China, the prediction accuracy, warning accuracy and prediction stability of the proposed hybrid framework outperformed the other comparative models. The analysis results indicate that the developed hybrid framework can be used as an effective tool for air pollutant prediction and early warning.

Observed causative impact of fine particulate matter on acute upper respiratory disease: a comparative study in two typical cities in China

Association between fine particulate matter (PM_2.5) and respiratory health has attracted great concern in China. Substantial epidemiological evidences confirm the correlational relationship between PM_2.5 and respiratory disease in many Chinese cities. However, the causative impact of PM_2.5 on respiratory disease remains uncertain and comparative analysis is limited. This study aims to explore and compare the correlational relationship as well as the causal connection between PM_2.5 and upper respiratory tract infection (URTI) in two typical cities (Beijing, Shenzhen) with rather different ambient air environment conditions. The distributed lag nonlinear model (DLNM) was used to detect the correlational relationship between PM_2.5 and URTI by revealing the lag effect pattern of PM_2.5 on URTI. The convergent cross mapping (CCM) method was applied to explore the causal connection between PM_2.5 and URTI. The results from DLNM indicate that an increase of 10 μg/m³ in PM_2.5 concentration is associated with an increase of 1.86% (95% confidence interval: 0.74%-2.99%) in URTI at a lag of 13 days in Beijing, compared with 2.68% (95% confidence interval: 0.99-4.39%) at a lag of 1 day in Shenzhen. The causality detection with CCM quantitatively demonstrates the significant causative influence of PM_2.5 on URTI in both two cities. Findings from the two methods consistently show that people living in low-concentration areas (Shenzhen) are less tolerant to PM_2.5 exposure than those in high-concentration areas (Beijing). In general, our study highlights the adverse health effects of PM_2.5 pollution on the general public in cities with various PM_2.5 levels and emphasizes the needs for the government to provide appropriate solutions to control PM_2.5 pollution, even in cities with low PM_2.5 concentration.

Meteorological Influences on Spatiotemporal Variation of PM2.5 Concentrations in Atmospheric Pollution Transmission Channel Cities of the Beijing-Tianjin-Hebei Region, China

Understanding the spatiotemporal characteristics of PM2.5 concentrations and identifying their associated meteorological factors can provide useful insight for implementing air pollution interventions. In this study, we used daily air quality monitoring data for 28 air pollution transmission channel cities in the Beijing-Tianjin-Hebei region during 2014-2019 to quantify the relative contributions of meteorological factors on spatiotemporal variation in PM2.5 concentration by combining time series and spatial perspectives. The results show that annual mean PM2.5 concentration significantly decreased in 24 of the channel cities from 2014 to 2019, but they all still exceeded the Grade II Chinese Ambient Air Quality Standards (35 μg m-3) in 2019. PM2.5 concentrations exhibited clear spatial agglomeration in the most polluted season, and their spatial pattern changed slightly over time. Meteorological variables accounted for 31.96% of the temporal variation in PM2.5 concentration among the 28 cities during the study period, with minimum temperature and average relative humidity as the most critical factors. Spatially, atmospheric pressure and maximum temperature played a key role in the distribution of PM2.5 concentration in spring and summer, whereas the effect of sunshine hours increased greatly in autumn and winter. These findings highlight the importance of future clean air policy making, but also provide a theoretical support for precise forecasting and prevention of PM2.5 pollution.

Exploring the convergence patterns of PM2.5 in Chinese cities

Economic development and ongoing urbanization are usually accompanied by severe haze pollution. Revealing the spatial and temporal evolution of haze pollution can provide a powerful tool for formulating sustainable development policies. Previous studies mostly discuss the differences in the level of PM2.5 among regions, but have paid little attention to the change rules of such differences and their clustering patterns over long periods. Therefore, from the perspective of club convergence, this study employs the log t regression test and club clustering algorithm proposed by Phillips and Sul (Econometrica 75(6):1771-1855, 2007. 10.1111/j.1468-0262.2007.00811.x) to empirically examine the convergence characteristics of PM2.5 concentrations in Chinese cities from 1998 to 2016. This study found that there was no evidence of full panel convergence, but supported one divergent group and eleven convergence clubs with large differences in mean PM2.5 concentrations and growth rates. The geographical distribution of these clubs showed significant spatial dependence. In addition, certain meteorological and socio-economic factors predominantly determined the convergence club for each city.

Seasonal Disparity in the Effect of Meteorological Conditions on Air Quality in China Based on Artificial Intelligence

Air contamination is identified with individuals’ wellbeing and furthermore affects the sustainable development of economy and society. This paper gathered the time series data of seven meteorological conditions variables of Beijing city from 1 November 2013 to 31 October 2017 and utilized the generalized regression neural network optimized by the particle swarm optimization algorithm (PSO-GRNN) to explore seasonal disparity in the impacts of mean atmospheric humidity, maximum wind velocity, insolation duration, mean wind velocity and rain precipitation on air quality index (AQI). The results showed that in general, the most significant impacting factor on air quality in Beijing is insolation duration, mean atmospheric humidity, and maximum wind velocity. In spring and autumn, the meteorological diffusion conditions represented by insolation duration and mean atmospheric humidity had a significant effect on air quality. In summer, temperature and wind are the most significant variables influencing air quality in Beijing; the most important reason for air contamination in Beijing in winter is the increase in air humidity and the deterioration of air diffusion condition. This study investigates the seasonal effects of meteorological conditions on air contamination and suggests a new research method for air quality research. In future studies, the impacts of different variables other than meteorological conditions on air quality should be assessed.

Composition analysis of PM2.5 at multiple sites in Zhengzhou, China: implications for characterization and source apportionment at different pollution levels

Zhengzhou is one of the most heavily polluted cities in China. This study collected samples of PM_2.5 (atmospheric fine particulate matter with aerodynamic diameter ≤ 2.5 μm) at five sites in different functional areas of Zhengzhou in 2016 to investigate the chemical properties and sources of PM_2.5 at three pollution levels, i.e., PM_2.5 ≤ 75 μg/m³ (non-pollution, NP), 75 μg/m³ < PM_2.5 ≤ 150 μg/m³ (moderate pollution, MP), and PM_2.5 > 150 μg/m³ (heavy pollution, HP). Chemical analysis was conducted, and source categories and potential source region were identified for PM_2.5 at different pollution levels. The health risks of toxic elements were evaluated. Results showed that the average PM_2.5 concentration in Zhengzhou was 119 μg/m³, and the sum of the concentrations of SO₄^2-, NO₃^-, and NH₄⁺ increased with the aggravation of pollution level (23, 42, and 114 μg/m³ at NP, MP, and HP days, respectively). Positive Matrix Factorization analysis indicated that secondary aerosols, coal combustion, vehicle traffic, industrial processes, biomass burning, and dust were the main sources of PM_2.5 at three pollution levels, and accounted for 38.4%, 21.6%, 16.7%, 7.4%, 7.7%, and 8.1% on HP days, respectively. Trajectory clustering analysis showed that close-range transport was one of the dominant factors on HP days in Zhengzhou. The potential source areas were mainly located in Xinxiang, Kaifeng, Xuchang, and Pingdingshan. Significant risks existed in the non-carcinogenic risk of As (1.4-2.3) for children at three pollution levels and the non-carcinogenic risk of Pb (1.0-1.4) for children with NP and MP days.

Reflections Based on Pollution Changes Brought by COVID-19 Lockdown in Shanghai

COVID-19 and its variants have been changing the world. The spread of variants brings severe effects to the global economy and to human's lives and health, as well as to society. Lockdown is proven to be effective in stopping the spread. It also provides a chance to study natural environmental changes with humanity's limited interference. This paper aims to evaluate the impact of lockdown on five major airborne pollutants, i.e., NO2, SO2, O3, PM2.5 and PM10, in the three different functional regions of Chongming, Xuhui and Jinshan of Shanghai. Changes in the same pollutants from the three regions over the same/different periods were all studied and compared. Overall, the COVID-19 lockdown has changed pollutant concentrations in the long and short terms. Concentrations of four pollutants decreased, except for that of earth surface O3, which increased. SO2 had significant correlations with all other pollutants. PM2.5 and PM10 are more externally input than locally produced. NO2, SO2 and PM levels sharply reduced in Jinshan and Xuhui due to the limited usage of fossil fuel. Lockdown improved the air quality. People now have a chance to rethink the value of life and the harmony between economic progress and environmental protection. This is helpful to establish sustainable societies.

A Comparison Analysis of Causative Impact of PM2.5 on Acute Exacerbation of Chronic Obstructive Pulmonary Disease (COPD) in Two Typical Cities in China

Chronic obstructive pulmonary disease (COPD) is a major and increasingly prevalent respiratory health problem worldwide and the fine particulate matter (PM2.5) is now becoming a rising health threat to it. This study aims to conduct a comparison analysis of health effect on acute exacerbation of COPD (AECOPD) associated with PM2.5 exposure in two typical cities (Beijing and Shenzhen) with different levels of PM2.5 pollution. Both correlational relationship and causal connection between PM2.5 exposure and AECOPD are investigated by adopting a time series analysis based on the generalized additive model (GAM) and convergent cross mapping (CCM). The results from GAM indicate that a 10 μg/m3 increase in PM2.5 concentration is associated with 2.43% (95% CI, 0.50–4.39%) increase in AECOPD on Lag0-2 in Beijing, compared with 6.65% (95% CI, 2.60–10.87%) on Lag0-14 in Shenzhen. The causality detection with CCM reveals similar significant causative impact of PM2.5 exposure on AECOPD in both two study areas. Findings from two methods agree that PM2.5 has non-negligible health effect on AECOPD in both two study areas, implying that air pollution can cause adverse consequences at much lower levels than common cognition. Our study highlights the adverse health effect of PM2.5 on people with COPD after exposure to different levels of PM2.5 and emphasizes that adverse effect in area with relative low pollution level cannot be overlooked. Governments in both high-pollution and low-pollution cities should attach importance to the adverse effects of PM2.5 on humans and take corresponding measures to control and reduce the related losses.

A Study on Indoor Particulate Matter Variation in Time Based on Count and Sizes and in Relation to Meteorological Conditions

An important aspect of air pollution analysis consists of the varied presence of particulate matter in analyzed air samples. In this respect, the present work aims to present a case study regarding the evolution in time of quantified particulate matter of different sizes. This study is based on data acquisitioned in an indoor location, already used in a former particulate matter-related article; thus, it can be considered as a continuation of that study, with the general aim to demonstrate the necessity to expand the existing network for pollution monitoring. Besides particle matter quantification, a correlation of the obtained results is also presented against meteorological data acquisitioned by the National Air Quality Monitoring Network. The transformation of quantified PM data in mass per volume and a comparison with other results are also addressed.

Impacts of meteorology and emission variations on the heavy air pollution episode in North China around the 2020 Spring Festival

Based on the Weather Research and Forecasting model and the Models-3 community multi-scale air quality model (WRF-CMAQ), this study analyzes the impacts of meteorological conditions and changes in air pollutant emissions on the heavy air pollution episode occurred over North China around the 2020 Spring Festival (January to Februray 2020). Regional reductions in air pollutant emissions required to eliminate the PM_2.5 heavy pollution episode are also quantified. Our results found that meteorological conditions for the Beijing-Tianjin-Hebei and surrounding "2+26" cities are the worst during the heavy pollution episode around the 2020 Spring Festival as compared with two other typical heavy pollution episodes that occurred after 2015. However, because of the substantial reductions in air pollutant emissions in the "2+26" cities in recent years, and the 32% extra reduction in emissions during January to February 2020 compared with the baseline emission levels of the autumn and winter of 2019 to 2020, the maximum PM_2.5 level during this heavy pollution episode around the 2020 Spring Festival was much lower than that in the other two typical episodes. Yet, these emission reductions are still not enough to eliminate regional heavy pollution episodes. Compared with the actual emission levels during January to February 2020, a 20% extra reduction in air pollutant emissions in the "2+26" cities (or a 45% extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020) could help to generally eliminate regionwide severe pollution episodes, and avoid heavy pollution episodes that last three or more consecutive days in Beijing; a 40% extra reduction in emissions (or a 60% extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020) could help to generally eliminate regionwide and continuous heavy pollution episodes. Our analysis finds that during the clean period after the heavy pollution episode around the 2020 Spring Festival, the regionwide heavy pollution episode would only occur with at least a 10-fold increase in air pollutant emissions.

Long-term characteristics of criteria air pollutants in megacities of Harbin-Changchun megalopolis, Northeast China: Spatiotemporal variations, source analysis, and meteorological effects

The hourly concentration of six criteria air pollutants in the Harbin-Changchun region were used to investigate the status and spatiotemporal variation of target air pollutants and their relationships with meteorological factors. The annual concentrations of particulate matters during 2013-2017 were two times higher than the Chinese Ambient Air Quality Standards (CAAQS) Grade Ⅱ. The annual O₃ concentration increased by two times during 2013-2018 in Harbin. The concentration of PM, SO₂, NO₂, and CO depicted a similar seasonal trend with an order of winter > autumn > spring > summer. The consistent interannual variation trends of PM_2.5/CO, NO₂ and SO₂ indicated that the formation of secondary inorganic aerosols in the annual scale was dominated by the concentrations of NO₂ and SO₂. The interannual variations of the individual meteorological factors causing on PM_2.5 and O₃ during 2013-2018 varied significantly in seasonal scale. The interannual variations were stable in annual scale indicating that the continuous decline of PM_2.5 during 2014-2018 can be attributed to the comprehensive and strict prohibition of small coal-fired boilers and straw burning in the study area. Meanwhile, the increase in O₃ during 2013-2018 in the study area were mainly attributed to the rapid growth of the emission of its precursor (VOCs and NOx). The influence of meteorology on PM_2.5 and ozone were the most stable and strongest in winter than that in the other three seasons.

Air quality improvement in response to intensified control strategies in Beijing during 2013-2019

Beijing's air pollution has become of increasing concern in recent years. The central and municipal governments have issued a series of laws, regulations, and strategies to improve ambient air quality. The "Clean Air Action" issued in 2013 and the "Comprehensive Action" issued in 2017 largely addressed this concern. In this study, we assessed the effectiveness of the two action plans by environmental monitoring data and evaluated the influencing factors including meteorology, pollutant emissions, and energy structure. The spatial distributions of air pollutants were analyzed using the Kriging interpolation method. The Principal Component Analysis-Multiple Nonlinear Regression (PCA-MNLR) model was applied to estimate the effects of meteorological factors. The results have shown that Beijing's air quality had a measurable improvement over 2013-2019. "Good air quality" days had the highest increases, and "hazardous air quality" days had the most decreases. SO₂ decreased most, followed by CO, PM_2.5, PM₁₀, and NO₂ in descending order, but O₃ showed a fluctuant increase. The "Comprehensive Action" was more effective than the "Clean Air Action" in reducing heavy pollution days during the heating period. The meteorological normalized values of the main pollutants were lower than the observation data during 2013-2016. However, the observed values became lower than the normalized values after 2017, which indicated beneficial weather conditions in 2017 and afterwards. The emissions of SO₂ and dust significantly decreased while NOx had a slight decrease, and the energy structure changed with a dramatic decrease in coal consumption and an obvious increase in the use of natural gas and electricity. The significant reduction of coal-fired emissions played a dominant role in improving Beijing's air quality, and vehicle emission control should be further enhanced. The results demonstrated the effectiveness of the two action plans and the experience in Beijing should have potential implications for other areas and nations suffering from severe air pollution.

Concentrations of Four Major Air Pollutants among Ecological Functional Zones in Shenyang, Northeast China

Air pollution is a critical urban environmental issue in China; however, the relationships between air pollutants and ecological functional zones in urban areas are poorly understood. Therefore, we analyzed the spatiotemporal characteristics of four major air pollutants (particulate matter less than or equal to 2.5 µm (PM2.5) and 10 µm (PM10) in diameter, SO2, and NO2) concentrations over five ecological functional zones in Shenyang, Liaoning Province, at hourly, seasonal, and annual scales using data collected from 11 monitoring stations over 2 years. We further assessed the relationships between these pollutants and meteorological conditions and land-use types at the local scale. Peaks in PM, SO2, and NO2 concentrations occurred at 08:00–09:00 and 23:00 in all five zones. Daytime PM concentrations were highest in the industrial zone, and those of SO2 and NO2 were highest in residential areas. All four air pollutants reached their highest concentrations in winter and lowest in summer. The highest mean seasonal PM concentrations were found in the industrial zone, and the highest SO2 and NO2 concentrations were found in residential areas. The mean annual PM and SO2 concentrations decreased in 2017 in all zones, while that of NO2 increased in all zones excluding the cultural zone. The natural reserve zone had the lowest concentrations of all pollutants at all temporal scales. Pollutant concentrations of PM2.5, PM10, SO2, and NO2 were correlated with visibility, and their correlation coefficients are 0.675, 0.579, 0.475, and 0.477. Land coverage with buildings and natural vegetation negatively and positively influence air pollutant concentrations, respectively.

Investigating PM2.5 responses to other air pollutants and meteorological factors across multiple temporal scales

It remains unclear on how PM_2.5 interacts with other air pollutants and meteorological factors at different temporal scales, while such knowledge is crucial to address the air pollution issue more effectively. In this study, we explored such interaction at various temporal scales, taking the city of Nanjing, China as a case study. The ensemble empirical mode decomposition (EEMD) method was applied to decompose time series data of PM_2.5, five other air pollutants, and six meteorological factors, as well as their correlations were examined at the daily and monthly scales. The study results show that the original PM_2.5 concentration significantly exhibited non-linear downward trend, while the decomposed time series of PM_2.5 concentration by EEMD followed daily and monthly cycles. The temporal pattern of PM₁₀, SO₂ and NO₂ is synchronous with that of PM_2.5. At both daily and monthly scales, PM_2.5 was positively correlated with CO and negatively correlated with 24-h cumulative precipitation. At the daily scale, PM_2.5 was positively correlated with O₃, daily maximum and minimum temperature, and negatively correlated with atmospheric pressure, while the correlation pattern was opposite at the monthly scale.

Effects of meteorological conditions and anthropogenic precursors on ground-level ozone concentrations in Chinese cities

Ground-level ozone pollution has negative impacts on human health and vegetation and has increased rapidly across China. Various factors are implicated in the formation of ozone (e.g., meteorological factors, anthropogenic emissions), but their relative individual impact and the impact of interactions between these factors remains unclear. This study quantified the influence of specific meteorological conditions and anthropogenic precursor emissions and their interactions on ozone concentrations in Chinese cities using the geographic detector model (GeoDetector). Results revealed that the impacts of meteorological and anthropogenic factors and their interactions on ozone concentrations varied significantly at different spatial and temporal scales. Temperature was the dominant driver at the annual time scale, explaining 40% (q = 0.4) of the ground-level ozone concentration. Anthropogenic precursors and meteorological conditions had comparable effects on ozone concentrations in summer and winter in northern China. Interactions between all the factors can enhance effects. The interaction between meteorological factors and anthropogenic precursors had the strongest impact in summer. The results can be used to enhance our understanding of ozone pollution, to improve ozone prediction models, and to formulate pollution control measures.

Investigation of the spatially varying relationships of PM2.5 with meteorology, topography, and emissions over China in 2015 by using modified geographically weighted regression

PM_2.5 pollution is caused by multiple factors and determining how these factors affect PM_2.5 pollution is important for haze control. In this study, we modified the geographically weighted regression (GWR) model and investigated the relationships between PM_2.5 and its influencing factors. Experiments covering 368 cities and 9 urban agglomerations were conducted in China in 2015 and more than 20 factors were considered. The modified GWR coefficients (MGCs) were calculated for six variables, including two emission factors (SO₂ and NO₂ concentrations), two meteorological factors (relative humidity and lifted index), and two topographical factors (woodland percentage and elevation). Then the spatial distribution of MGCs was analyzed at city, cluster, and region scales. Results showed that the relationships between PM_2.5 and the different factors varied with location. SO₂ emission positively affected PM_2.5, and the impact was the strongest in the Beijing-Tianjin-Hebei (BTH) region. The impact of NO₂ was generally smaller than that of SO₂ and could be important in coastal areas. The impact of meteorological factors on PM_2.5 was complicated in terms of spatial variations, with relative humidity and lifted index exerting a strong positive impact on PM_2.5 in Pearl River Delta and Central China, respectively. Woodland percentage mainly influenced PM_2.5 in regions of or near deserts, and elevation was important in BTH and Sichuan. The findings of this study can improve our understanding of haze formation and provide useful information for policy-making.

Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism

Air pollution over China has attracted wide interest from public and academic community. PM_2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM_2.5 concentrations are essential to understand the variability of PM_2.5 and seek methods to control PM_2.5. Since 2013, the measurement of PM_2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM_2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM_2.5 concentrations_. We start with an introduction of general meteorological conditions and PM_2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM_2.5 concentrations. Next, major methods used to quantify meteorological influences on PM_2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM_2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM_2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM_2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM_2.5. The feedback effects of PM_2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM_2.5 pollution are made finally.

The unidirectional causality influence of factors on PM2.5 in Shenyang city of China

Air quality issue such as particulate matter pollution (PM_2.5 and PM₁₀) has become one of the biggest environmental problem in China. As one of the most important industrial base and economic core regions of China, Northeast China is facing serious air pollution problems in recent years, which has a profound impact on the health of local residents and atmospheric environment in some part of East Asia. Therefore, it is urgent to understand temporal-spatial characteristics of particles and analyze the causality factors. The results demonstrated that variation trend of particles was almost similar, the annual, monthly and daily distribution had their own characteristics. Particles decreased gradually from south to north, from west to east. Correlation analysis showed that wind speed was the most important factor affecting particles, and temperature, air pressure and relative humidity were key factors in some seasons. Path analysis showed that there was complex unidirectional causal relationship between particles and individual or combined effects, and NO₂ and CO were key factors affecting PM_2.5. The hot and cold areas changed little with the seasons. All the above results suggests that planning the industrial layout, adjusting industrial structure, joint prevention and control were necessary measure to reduce particles concentration.

Monitoring history and change trends of ambient air quality in China during the past four decades

This study summarized the history of ambient air quality monitoring and air pollution prevention and control, and it analyzed the spatiotemporal patterns of ambient air pollutants during 1981-2017 in China. The results showed that monitoring of ambient air quality has changed dramatically in terms of determinants, sampling methods, monitoring extent, and evaluation basis during the previous four decades. Annual average concentrations of total suspended particulates, PM₁₀ and SO₂ have shown obvious decreasing trends during the studied period. These improvements have been closely related to the considerable efforts and various approaches undertaken to prevent and control air pollution. However, although policy implementation has been decisive and, at least in part, it has been enforced effectively, significant challenges remain. Air pollution control cannot be accomplished without a long-term strategy designed to achieve clean air in all parts of China.

Temporal variations of ambient air pollutants and meteorological influences on their concentrations in Tehran during 2012-2017

We investigated temporal variations of ambient air pollutants and the influences of meteorological parameters on their concentrations using a robust method; convergent cross mapping; in Tehran (2012–2017). Tehran citizens were consistently exposed to annual PM_2.5, PM₁₀ and NO₂ approximately 3.0–4.5, 3.5–4.5 and 1.5–2.5 times higher than the World Health Organization air quality guideline levels during the period. Except for O₃, all air pollutants demonstrated the lowest and highest concentrations in summertime and wintertime, respectively. The highest O₃ concentrations were found on weekend (weekend effect), whereas other ambient air pollutants had statistically significant (P < 0.05) daily variations in which higher concentrations were observed on weekdays compared to weekend (holiday effect). Hourly O₃ concentration reached its peak at 3.00 p.m., though other air pollutants displayed two peaks; morning and late night. Approximately 45% to 65% of AQI values were in the subcategory of unhealthy for sensitive groups and PM_2.5 was the responsible air pollutant in Tehran. Amongst meteorological factors, temperature was the key influencing factor for PM_2.5 and PM₁₀ concentrations, while nebulosity and solar radiation exerted major influences on ambient SO₂ and O₃ concentrations. Additionally, there is a moderate coupling between wind speed and NO₂ and CO concentrations.

Estimating the effect of urbanization on extreme climate events in the Beijing-Tianjin-Hebei region, China

Quantifying the impact of urbanization on extreme climate events is significant for ecosystem responses, flood control, and urban planners. This study aimed to examine the urbanization effects on a suite of 36 extreme temperature and precipitation indices for the Beijing-Tianjin-Hebei (BTH) region by classifying the climate observations into three different urbanization levels. A total of 176 meteorological stations were used to identify large cities, small and medium-size cities and rural environments by applying K-means cluster analysis combined with spatial land use, nighttime light remote sensing, socio-economic data and Google Earth. The change trends of the extreme events during 1980-2015 were detected by using Mann-Kendall (MK) statistical test and Sen's slope estimator. Urbanization effects on those extreme events were calculated as well. Results indicated that the cool indices generally showed decreasing trends over the time period 1980-2015, while the warm indices tended to increase. Larger and more significant changes occurred with indices related to the daily minimum temperature. The different change rates of temperature extremes in urban, suburban and rural environments were mainly about the cool and warm night indices. Urbanization in medium-size cities tended to have a negative effect on cool indices, while the urbanization in large cities had a positive effect on warm indices. The significant difference of urbanization effect between large and medium-size cities lay in the daily maximum temperature. Results also demonstrated the scale effect of the urbanization on the extreme temperature events. However, the results showed little evidence of the urban effect on extreme precipitation events in the BTH region. This paper explored the changes in temperature and precipitation extremes and qualified the urbanization effects on those extreme events in the BTH region. The findings of this research can provide new insights into the future urban agglomeration development projects.

Spatiotemporal Trend Analysis of PM2.5 Concentration in China, 1999–2016

China is experiencing severe PM 2 . 5 (fine particles with a diameter of 2.5 μ g or smaller) pollution problem. Little is known, however, about how the increasing concentration trend is spatially distributed, nor whether there are some areas that experience a stable or decreasing concentration trend. Managers and policymakers require such information to make strategic decisions and monitor progress towards management objectives. Here, we present a pixel-based linear trend analysis of annual PM 2 . 5 concentration variation in China during the period 1999–2016, and our results provide guidance about where to prioritize management efforts and affirm the importance of controlling coal energy consumption. We show that 87.9% of the whole China area had an increasing trend. The drastic increasing trends of PM 2 . 5 concentration during the last 18 years in the Beijing–Tianjin–Hebei region, Shandong province, and the Three Northeastern Provinces are discussed. Furthermore, by exploring regional PM 2 . 5 pollution, we find that Tarim Basin endures a high PM 2 . 5 concentration, and this should have some relationship with oil exploration. The relationship between PM 2 . 5 pollution and energy consumption is also discussed. Not only energy structure reconstruction should be repeatedly emphasized, the amount of coal burned should be strictly controlled.

Impact assessment of meteorological and environmental parameters on PM2.5 concentrations using remote sensing data and GWR analysis (case study of Tehran)

The PM_2.5 as one of the main pollutants in Tehran city has a devastating effect on human health. Knowing the key parameters associated with PM_2.5 concentration is essential to take effective actions to reduce the concentration of these particles. This study assesses the relationship between meteorological (humidity, pressure, temperature, precipitation, and wind speed) and environmental parameters (normalize difference vegetation index and land surface temperature of MODIS satellite data) on PM_2.5 concentration in Tehran city. The Geographically Weighted Regression (GWR) was employed to assess the impact of key parameters on PM_2.5 concentrations in winter and summer. For this purpose, first the seasonal average of meteorological data were extracted and synchronized to satellite data. Then, using the ordinary least square model, the important parameters related to PM_2.5 concentration were determined and evaluated. Finally, using the GWR model, the relationships between parameters related to PM_2.5 concentration were analyzed. The results of this study indicate that meteorological and environmental parameters in winter season (71%) have a much higher ability to explain PM_2.5 concentration than summer season (40%). In winter, PM_2.5 concentration has a negative correlation with vegetation at most parts of the study area, a negative correlation with LST in the western and a positive correlation in the eastern part of the study area, a positive correlation with temperature, and a negative correlation with wind speed in the northeastern part of the study area. Precipitation has a positive correlation with PM_2.5 concentration in most parts of the study area in both seasons. But, it was investigated in case of higher precipitation (more than 2 mm), PM_2.5 concentration decreases. But, there is no negative relationship in any of the dependent parameters with PM_2.5 concentration in summer. In this season, the air temperature parameter showed a high correlation with PM_2.5 concentration. Also, spatial variations of the local coefficients for all parameters are higher in winter than in summer.

Microclimate simulation and model optimization of the effect of roadway green space on atmospheric particulate matter

Urban green spaces have the potential to mitigate and regulate atmospheric pollution. However, existing studies have primarily focused on the adsorption effect of different plants on atmospheric particulate matter (PM), whereas the effect of green space on PM has not been adequately addressed. In this study, the effect of different urban green space structures and configurations on PM was investigated through the 3D computational fluid dynamics (CFD) model ENVI-met by treating the green space as a whole based on field monitoring, and at the same time, the regulatory effect of green space on PM was examined by integrating information about the forest stand, PM concentration, and meteorological factors. The results show that the green space primarily affected wind speed but had no significant effect on relative humidity, temperature, or wind direction (P > 0.05). The PM concentration was significantly positively correlated with the relative humidity (P < 0.01), significantly negatively correlated with temperature (P < 0.05), but not significantly correlated with wind speed and direction (P > 0.05). Comparison with the measured values reveals that the ENVI-met model well reflected the differences in PM concentrations between different green spaces and the effect of green space on PM. In different green space structures, the uniform-type structure performed rather poorly at purifying PM, the concave-shaped structure performed the best, and the purifying effectiveness of the incremental-type and convex-shaped structure of green space was higher in the rear region than in the front region; in contrast, the degressional-type green space structure was prone to cause aggregation of the PM in the middle region. Broadleaf and broadleaf mixed forests had a better purifying effectiveness on PM than did coniferous forests, mixed coniferous forests, and coniferous broadleaf mixed forests. The above results are of great significance for urban planning and maximizing the use of urban green space resources.

Understanding long-term variations of meteorological influences on ground ozone concentrations in Beijing During 2006-2016

Recently, ground ozone has become one major airborne pollutant and the frequency of ozone-induced pollution episodes has increased rapidly across China. However, due to the lack of long-term observation data, relevant research on the characteristics and influencing factors of urban ozone concentrations remains limited. Based on ground ozone observation data during 2006-2016, we quantified the causality influence of individual meteorological factors on ozone concentrations in Beijing using a convergent cross mapping (CCM) method. The result indicated that the influence of each meteorological factor on ozone concentrations varied significantly across seasons and years. At the inter-annual scale, all-year meteorological influences on ozone concentrations were much more stable than seasonal meteorological influences. At the seasonal scale, meteorological influences on ozone concentrations were stronger in spring and autumn. Amongst multiple individual factors, temperature was the key meteorological influencing factor for ozone concentrations in all seasons except winter, when wind, humidity and SSD exerted major influences on ozone concentrations. In addition to temperature, air pressure was another meteorological factor that exerted strong influences on ozone concentrations. At both the inter-annual and seasonal scale, the influence of temperature and humidity on ozone concentrations was generally stable whilst that of other factors experienced large variations. Different from PM_2.5, meteorological influences on ozone concentrations were relatively weak in summer, when ozone concentrations were the highest in Beijing. Given the generally stable meteorological influences on ozone concentrations and human-induced emissions of VOCs and NO_x across seasons, warming induced notable increase in summertime biogenic emissions of VOCs and NO_x can be a major driver for the increasing ozone pollution episodes. This research provides useful references for understanding long-term meteorological influences on ozone concentrations in mega cities in China.

Temporal variations and potential sources of organophosphate esters in PM2.5 in Xinxiang, North China

We monitored the concentrations of 10 organophosphate esters (OPEs) in 52 fine particulate matter (PM_2.5) samples in Xinxiang, Henan Province, North China, in 2015. During the sampling period, the OPE concentrations in most samples (n = 47) differed minimally and were relatively stable (mean: 2.02 ± 0.93 ng m^-3), although several samples (n = 5) had high total OPE (Ʃ₁₀OPE) concentrations (mean: 9.99 ± 5.69 ng m^-3), which may have been influenced by high PM_2.5 levels. Meanwhile, some samples had high PM_2.5 concentrations but low Ʃ₁₀OPE concentrations (i.e. low OPE/PM_2.5 ratios) or low PM_2.5 concentrations but high Ʃ₁₀OPE concentrations, which might have been influenced by air mass sources. Therefore, we assessed air mass sources using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and wind direction frequency data, and subsequently analysed PM_2.5 and OPE sources using a potential source contribution function (PSCF) model. The results revealed that air mass sources couldn't represent the source of specific pollutants, including PM_2.5 and OPEs. Generally, both PM_2.5 and OPEs were from Henan and Shandong Provinces; however, the major source areas differed, which may have resulted from diverse pollution characteristics in various source areas. The principal component analysis and PSCF results revealed that the 10 OPEs could be segmented into three groups, which were associated with different source areas. These results suggested that pollution characteristics of contaminants in source areas should be considered in source apportionment.

Impact of Meteorological Conditions on PM2.5 Pollution in China during Winter

Fine particulate matter (PM2.5) poses a risk to human health. In January 2017, the PM2.5 pollution in China was severe, and the average PM2.5 concentration had increased by 14.7% compared to that in January 2016. Meteorological conditions greatly influence PM2.5 pollution. The relationship between PM2.5 and meteorological factors was assessed using monitoring data and the Community Multiscale Air Quality modeling system (CMAQ) was used to quantitatively evaluate the impacts of variations of meteorological conditions on PM2.5 pollution. The results indicate that variations of meteorological conditions between January 2017 and January 2016 caused an increase of 13.6% in the national mean concentration of PM2.5. Unlike the Yangtze River Delta (YRD), where meteorological conditions were favorable, unfavorable meteorological conditions (such as low wind speed, high humidity, low boundary layer height and low rainfall) contributed to PM2.5 concentration worsening by 29.7%, 42.6% and 7.9% in the Beijing-Tianjin-Hebei (JJJ) region, the Pearl River Delta (PRD) region and the Chengdu-Chongqing (CYB) region, respectively. Given the significant influence of local meteorology on PM2.5 concentration, more emphasis should be placed on employing meteorological means to improve local air quality.

Spatiotemporal Dynamics of Green Spaces in the Beijing–Tianjin–Hebei Region in the Past 20 Years

Rapid urbanization has caused the reduction of green spaces in most cities, disrupting the structure and process of urban and rural ecosystems. The accurate identification of spatiotemporal changes in green spaces is important to delineate future management and planning. We investigated green space types of the Beijing–Tianjin–Hebei region in 1995, 2000, 2005, 2010, and 2015 based on the elevation data and land use/cover for those years. Spatiotemporal changes in these identified green spaces between 1995 and 2015 were evaluated as well as the spatial hotspots of disappeared and unstable green patches. The results indicate that the cultivated land in plains and forests and cultivated land in medium-high mountainous areas were the main green space types in the Beijing–Tianjin–Hebei region during the period from 1995 to 2015. A large number of green spaces, in particular cultivated lands, in the peripheral areas of big cities were replaced by construction sites over the past 20 years. Hotspots of unstable green spaces were mainly distributed in the western and northern mountainous areas of the Beijing–Tianjin–Hebei region, where green spaces changed from one type to another. These findings provide an important reference for the management and planning of land and green spaces towards an integrative and collaborative development of the Beijing-Tianjin-Hebei region.

Understanding the Influence of Crop Residue Burning on PM2.5 and PM10 Concentrations in China from 2013 to 2017 Using MODIS Data

In recent years, particulate matter (PM) pollution has increasingly affected public life and health. Therefore, crop residue burning, as a significant source of PM pollution in China, should be effectively controlled. This study attempts to understand variations and characteristics of PM₁₀ and PM_2.5 concentrations and discuss correlations between the variation of PM concentrations and crop residue burning using ground observation and Moderate Resolution Imaging Spectroradiometer (MODIS) data. The results revealed that the overall PM concentration in China from 2013 to 2017 was in a downward tendency with regional variations. Correlation analysis demonstrated that the PM₁₀ concentration was more closely related to crop residue burning than the PM_2.5 concentration. From a spatial perspective, the strongest correlation between PM concentration and crop residue burning existed in Northeast China (NEC). From a temporal perspective, the strongest correlation usually appeared in autumn for most regions. The total amount of crop residue burning spots in autumn was relatively large, and NEC was the region with the most intense crop residue burning in China. We compared the correlation between PM concentrations and crop residue burning at inter-annual and seasonal scales, and during burning-concentrated periods. We found that correlations between PM concentrations and crop residue burning increased significantly with the narrowing temporal scales and was the strongest during burning-concentrated periods, indicating that intense crop residue burning leads to instant deterioration of PM concentrations. The methodology and findings from this study provide meaningful reference for better understanding the influence of crop residue burning on PM pollution across China.

Ground ozone concentrations over Beijing from 2004 to 2015: Variation patterns, indicative precursors and effects of emission-reduction

Based on ozone observation data from urban stations and the Dingling (DL) background station, we investigated the trend of ozone concentrations in Beijing during 2004-2015. For urban stations, both O₃_1 h and O₃_8 h increased stably with a clear and significant linear pattern and the increase rate was notably higher during the period of May to Sep. Meanwhile, the variation of O₃_1 h and O₃_8 h for the DL station did not demonstrate a regular pattern. During this period, the differences between the diurnal peak of ozone concentrations at the DL background station and urban stations decreased significantly due to the rapid urbanization of Beijing. Furthermore, we examined simultaneous variations of ozone and its precursors during 2015 Grand Military Parade and 2014 APEC meeting and evaluated the performances of different emission-reduction measures during the two specific events. For 2015 Grand Military Parade, emission-reduction measures were implemented 14 days in advance, which led to a notable decrease of ozone concentrations during the Parade period. For 2014 APEC meeting, emission-reduction measures were not implemented in advance, which led to incomplete VOCs reduction and high VOCs/NO_x values, and thus a significant increase of ozone concentrations during the APEC period. The emission-reduction measures during APEC and PARADE periods both slowed down the accumulation and cut down the concentration peaks of ozone. We also analyzed simultaneous concentration variations of ozone and its precursors in long time-series. The results proved that compared with other precursors, NO₂/NO was an effective indicator for ozone concentration in Beijing, especially in urban areas. The findings from this research provide useful reference for better monitoring and managing ozone concentrations in Beijing and other cities through properly designed and implemented emission-reduction measures.

Visualizing the intercity correlation of PM2.5 time series in the Beijing-Tianjin-Hebei region using ground-based air quality monitoring data

The Beijing-Tianjin-Hebei area faces a severe fine particulate matter (PM_2.5) problem. To date, considerable progress has been made toward understanding the PM_2.5 problem, including spatial-temporal characterization, driving factors, and health effects. However, little research has been done on the dynamic interactions and relationships between PM_2.5 concentrations in different cities in this area. To address the research gap, this study discovered a phenomenon of time-lagged intercity correlations of PM_2.5 time series and proposed a visualization framework based on this phenomenon to visualize the interaction in PM_2.5 concentrations between cities. The visualizations produced using the framework show that there are significant time-lagged correlations between the PM_2.5 time series in different cities in this area. The visualizations also show that the correlations are more significant in colder months and between cities that are closer, and that there are seasonal changes in the temporal order of the correlated PM_2.5 time series. Further analysis suggests that the time-lagged intercity correlations of PM_2.5 time series are most likely due to synoptic meteorological variations. We argue that the visualizations demonstrate the interactions of air pollution between cities in the Beijing-Tianjin-Hebei area and the significant effect of synoptic meteorological conditions on PM_2.5 pollution. The visualization framework could help determine the pathway of regional transportation of air pollution and may also be useful in delineating the area of interaction of PM_2.5 pollution for impact analysis.

Spatiotemporal Variations and Driving Factors of Air Pollution in China

In recent years, severe and persistent air pollution episodes in China have drawn wide public concern. Based on ground monitoring air quality data collected in 2015 in Chinese cities above the prefectural level, this study identifies the spatiotemporal variations of air pollution and its associated driving factors in China using descriptive statistics and geographical detector methods. The results show that the average air pollution ratio and continuous air pollution ratio across Chinese cities in 2015 were 23.1 ± 16.9% and 16.2 ± 14.8%. The highest levels of air pollution ratio and continuous air pollution ratio were observed in northern China, especially in the Bohai Rim region and Xinjiang province, and the lowest levels were found in southern China. The average and maximum levels of continuous air pollution show distinct spatial variations when compared with those of the continuous air pollution ratio. Monthly changes in both air pollution ratio and continuous air pollution ratio have a U-shaped variation, indicating that the highest levels of air pollution occurred in winter and the lowest levels happened in summer. The results of the geographical detector model further reveal that the effect intensity of natural factors on the spatial disparity of the air pollution ratio is greater than that of human-related factors. Specifically, among natural factors, the annual average temperature, land relief, and relative humidity have the greatest and most significant negative effects on the air pollution ratio, whereas human factors such as population density, the number of vehicles, and Gross Domestic Product (GDP) witness the strongest and most significant positive effects on air pollution ratio.

The Relationships between PM2.5 and Meteorological Factors in China: Seasonal and Regional Variations

The interactions between PM_2.5 and meteorological factors play a crucial role in air pollution analysis. However, previous studies that have researched the relationships between PM_2.5 concentration and meteorological conditions have been mainly confined to a certain city or district, and the correlation over the whole of China remains unclear. Whether spatial and seasonal variations exist deserves further research. In this study, the relationships between PM_2.5 concentration and meteorological factors were investigated in 68 major cities in China for a continuous period of 22 months from February 2013 to November 2014, at season, year, city, and regional scales, and the spatial and seasonal variations were analyzed. The meteorological factors were relative humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS). We found that spatial and seasonal variations of their relationships with PM_2.5 exist. Spatially, RH is positively correlated with PM_2.5 concentration in north China and Urumqi, but the relationship turns to negative in other areas of China. WS is negatively correlated with PM_2.5 everywhere except for Hainan Island. PS has a strong positive relationship with PM_2.5 concentration in northeast China and mid-south China, and in other areas the correlation is weak. Seasonally, the positive correlation between PM_2.5 concentration and RH is stronger in winter and spring. TEM has a negative relationship with PM_2.5 in autumn and the opposite in winter. PS is more positively correlated with PM_2.5 in autumn than in other seasons. Our study investigated the relationships between PM_2.5 and meteorological factors in terms of spatial and seasonal variations, and the conclusions about the relationships between PM_2.5 and meteorological factors are more comprehensive and precise than before. We suggest that the variations could be considered in PM_2.5 concentration prediction and haze control to improve the prediction accuracy and policy efficiency.

Spatio-temporal variations of PM2.5 concentrations and the evaluation of emission reduction measures during two red air pollution alerts in Beijing

To effectively improve air quality during pollution episodes, Beijing released two red alerts in 2015. Here we examined spatio-temporal variations of PM_2.5 concentrations during two alerts based on multiple data sources. Results suggested that PM_2.5 concentrations varied significantly across Beijing. PM_2.5 concentrations in southern parts of Beijing were higher than those in northern areas during both alerts. In addition to unfavorable meteorological conditions, coal combustion, especially incomplete coal combustion contributed significantly to the high PM_2.5 concentrations. Through the CAMx model, we evaluated the effects of emission-reduction measures on PM_2.5 concentrations. Through simulation, emergency measures cut down 10% - 30% of the total emissions and decreased the peaks of PM_2.5 concentrations by about 10-20% during two alerts. We further examined the scenario if emergency measures were implemented several days earlier than the start of red alerts. The results proved that the implementation of emission reduction measures 1-2 days before red alerts could lower the peak of PM_2.5 concentrations significantly. Given the difficulty of precisely predicting the duration of heavy pollution episodes and the fact that successive heavy pollution episodes may return after red alerts, emergency measures should also be implemented one or two days after the red alerts.