Effects of Meteorological Conditions on PM2.5 Concentrations in Nagasaki, Japan

First temporal distribution model of ambient air pollutants (PM2.5, PM10, and O3) in Yangon City, Myanmar during 2019-2021

Air pollution has emerged as a significant global concern, particularly in urban centers. This study aims to investigate the temporal distribution of air pollutants, including PM2.5, PM10, and O3, utilizing multiple linear regression modeling. Additionally, the research incorporates the calculation of the Air Quality Index (AQI) and Autoregressive Integrated Moving Average (ARIMA) time series modeling to predict the AQI for PM2.5 and PM10. The concentrations and AQI values for PM2.5 ranged from 0 to 93.6 μg/m3 and 0 to 171, respectively, surpassing the Word Health Organization's (WHO) acceptable threshold levels. Similarly, concentrations and AQI values for PM10 ranged from 0.1 to 149.27 μg/m3 and 2-98 μg/m3, respectively, also exceeding WHO standards. Particulate matter pollution exhibited notable peaks during summer and winter. Key meteorological factors, including dew point temperature, relative humidity, and rainfall, showed a significant negative association with all pollutants, while ambient temperature exhibited a significant positive correlation with particulate matter. Multiple linear regression models of particulate matter for winter season demonstrated the highest model performance, explaining most of the variation in particulate matter concentrations. The annual multiple linear regression model for PM2.5 exhibited the most robust performance, explaining 60% of the variation, while the models for PM10 and O3 explained 45% of the variation in their concentrations. Time series modeling projected an increasing trend in the AQI for particulate matter in 2022. The precise and accurate results of this study serve as a valuable reference for developing effective air pollution control strategies and raising awareness of AQI in Myanmar.

Spatiotemporal empirical analysis of particulate matter PM2.5 pollution and air quality index (AQI) trends in Africa using MERRA-2 reanalysis datasets (1980-2021)

In this study, the spatial-temporal trends of PM2.5 pollution were analyzed for subregions in Africa and the entire continent from 1980 to 2021. The distributions and trends of PM2.5 were derived from the monthly concentrations of the aerosol species from MERRA-2 reanalysis datasets comprising of sulphates (SO4), organic carbon (OC), black carbon (BC), Dust2.5 and Sea Salt (SS2.5). The resulting PM2.5 trends were compared with the climate factors, socio-economic indicators, and terrain characteristics. Using the Mann-Kendall (M-K) test, the continent and its subregions showed positive trends in PM2.5 concentrations, except for western and central Africa which exhibited marginal negative trends. The M-K trends also determined Dust2.5 as the dominant contributing aerosol factor responsible for the high PM2.5 concentrations in the northern, western and central regions of Africa, while SO4 and OC were respectively the most significant contributors to PM2.5 in the eastern and southern Africa regions. For the climate factors, the PM2.5 trends were determined to be positively correlated with the wind speed trends, while precipitation and temperature trends exhibited low and sometimes negative correlations with PM2.5. Socio-economically, highly populated, and bare/sparse vegetated areas showed higher PM2.5 concentrations, while vegetated areas tended to have lower PM2.5 concentrations. Topographically, low laying regions were observed to retain the deposited PM2.5 especially in the northern and western regions of Africa. The Air Quality Index (AQI) results showed that 94 % of the continent had an average PM2.5 of 12-35 μg/m3 hence classified as "Moderate" AQI, and the rest of the continent's PM2.5 levels was between 35 and 55 μg/m3 implying AQI classification of "Unhealthy for Sensitive People". Northern and western Africa regions had the highest AQI, while southern Africa had the lowest AQI. The approach and findings in this study can be used to complement the evaluation and management of air quality in Africa.

Wintertime investigation of PM10 concentrations, sources, and relationship with different meteorological parameters

Meteorological factors play a crucial role in affecting air quality in the urban environment. Peshawar is the capital city of the Khyber Pakhtunkhwa province in Pakistan and is a pollution hotspot. Sources of PM10 and the influence of meteorological factors on PM10 in this megacity have yet to be studied. The current study aims to investigate PM10 mass concentration levels and composition, identify PM10 sources, and quantify links between PM10 and various meteorological parameters like temperature, relative humidity (RH), wind speed (WS), and rainfall (RF) during the winter months from December 2017 to February 2018. PM10 mass concentrations vary from 180 - 1071 µg m-3, with a mean value of 586 ± 217 µg m-3. The highest concentration is observed in December, followed by January and February. The average values of the mass concentration of carbonaceous species (i.e., total carbon, organic carbon, and elemental carbon) are 102.41, 91.56, and 6.72 μgm-3, respectively. Water-soluble ions adhere to the following concentration order: Ca2+  > Na+  > K+  > NH4+  > Mg2+. Twenty-four elements (Al, Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Co, Zn, Ga, Ge, As, Se, Kr, Ag, Pb, Cu, and Cd) are detected in the current study by PIXE analysis. Five sources based on Positive Matrix Factorization (PMF) modeling include industrial emissions, soil and re-suspended dust, household combustion, metallurgic industries, and vehicular emission. A positive relationship of PM10 with temperature and relative humidity is observed (r = 0.46 and r = 0.56, respectively). A negative correlation of PM10 is recorded with WS (r =  - 0.27) and RF (r =  - 0.46). This study's results motivate routine air quality monitoring owing to the high levels of pollution in this region. For this purpose, the establishment of air monitoring stations is highly suggested for both PM and meteorology. Air quality standards and legislation need to be revised and implemented. Moreover, the development of effective control strategies for air pollution is highly suggested.

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.

Hourly forecasting on PM2.5 concentrations using a deep neural network with meteorology inputs

The PM2.5 (particulate matter with a diameter of fewer than 2.5 µm) has become a global topic in environmental science. The neural network that based on the non-linear regression algorithm, e.g., deep learning, is now believed to be one of the most facile and advanced approaches in PM2.5 concentration prediction. In this study, we proposed a PM2.5 predictor using deep learning as infrastructure and meteorological data as input, for predicting the next hour PM2.5 concentration in Beijing Aotizhongxin monitor point. We efficiently use the parameter's spatiotemporal correlation by concatenating the dataset with time series. The predicted PM2.5 concentration was based on meteorology changes over a period. Therefore, the accuracy would increase with the period growing. By extracting the intrinsic features between meteorological and PM2.5 concentration, a fast and accurate prediction was carried out. The R square score reached maximum of 0.98 and remained an average of 0.9295 in the whole test. The average bias of the model is 9 μg on the validation set and 1 μg on the training set. Moreover, the differences between the predictions and expectations can be further regarded as the estimation for the emission change. Such results can provide scientific advice to supervisory and policy workers.

Strategies for the coordinated control of particulate matter and carbon dioxide under multiple combined pollution conditions

Air pollutants represented by fine particulate matter (PM2.5) and the greenhouse effect caused by carbon dioxide (CO2), are both urgent threats to public health. Tackling the synergistic reduction of PM2.5 and CO2 is critical to achieving improvements in clean air worldwide. A persistent issue is the identification of their common sources and integrated impacts under different environmental conditions. In this study, we investigated the characteristics of the pollution types captured by combined analysis through a comprehensive observational dataset for 2017-2020, and applied machine learning algorithms to quantify the effects of drivers on air pollutants and CO2 formation. More importantly, detailed conclusions were drawn for the joint control of PM2.5-CO2 in multiple pollution types by using ensemble traceability technique. We demonstrated that reducing coal combustion emissions was an effective measure to maximize the benefits of PM2.5-CO2 in weather with low CO2 levels and no PM2.5 pollution. Correspondingly, on days with severe PM2.5 episodes, prioritizing control of vehicle emissions can simultaneously mitigate PM2.5 and CO2. Similar conclusions were found at high CO2 levels, accompanied by a more extensive role of vehicle emissions. Furthermore, a comparison of the differences in source impacts between PM2.5-CO2 and individual species suggests that focusing only on the sources that contribute significantly to one species may result in an underestimation or overestimation of PM2.5-CO2 source impacts. One such implication, as evidenced by our findings, is that synergistic controlling common sources of pollutants should be efficient. Thereby, common source management targeting PM2.5-CO2 under multiple pollution types is a more workable solution to alleviate environmental pollution.

Generating High Spatial Resolution Exposure Estimates from Sparse Regulatory Monitoring Data

Random Forest algorithms have extensively been used to estimate ambient air pollutant concentrations. However, the accuracy of model-predicted estimates can suffer from extrapolation problems associated with limited measurement data to train the machine learning algorithms. In this study, we developed and evaluated two approaches, incorporating low-cost sensor data, that enhanced the extrapolating ability of random-forest models in areas with sparse monitoring data. Rochester, NY is the area of a pregnancy-cohort study. Daily PM2.5 concentrations from the NAMS/SLAMS sites were obtained and used as the response variable in the model, with satellite data, meteorological, and land-use variables included as predictors. To improve the base random-forest models, we used PM2.5 measurements from a pre-existing low-cost sensors network, and then conducted a two-step backward selection to gradually eliminate variables with potential emission heterogeneity from the base models. We then introduced the regression-enhanced random forest method into the model development. Finally, contemporaneous urinary 1-hydroxypyrene was used to evaluate the PM2.5 predictions generated from the two approaches. The two-step approach increased the average external validation R2 from 0.49 to 0.65, and decreased the RMSE from 3.56 μg/m3 to 2.96 μg/m3. For the regression-enhanced random forest models, the average R2 of the external validation was 0.54, and the RMSE was 3.40 μg/m3. We also observed significant and comparable relationships between urinary 1-hydroxypyrene levels and PM2.5 predictions from both improved models. This PM2.5 model estimation strategy could improve the extrapolating ability of random forest models in areas with sparse monitoring data.

Effect of lockdown and associated mobility changes amid COVID-19 on air quality in the Kathmandu Valley, Nepal

The COVID-19 pandemic caused a setback for Nepal, leading to nationwide lockdowns. The study analyzed the impact of lockdown on air quality during the first and second waves of the COVID-19 pandemic in the Kathmandu Valley. We analyzed 5 years of ground-based air quality monitoring data (2017-2021) from March to July and April to June for the first and second wave lockdowns, respectively. A significant decrease in PM2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 μm) concentrations was observed during the lockdowns. The highest rate of decline in PM2.5 levels was observed during May and July compared to the pre-pandemic year. The PM2.5 concentration during the lockdown period remained within the WHO guideline limit and NAAQS for the maximum number of days compared to the lockdown window in the pre-pandemic years (2017-2019). Likewise, lower PM2.5 levels were observed during the second wave lockdown, which was characterized by a targeted lockdown approach (smart lockdown). We found a significant correlation of PM2.5 concentration with community mobility changes (i.e., walking, driving, and using public transport) from the Spearman correlation analysis. Lockdown measures restricted human mobility that led to a lowering of PM2.5 concentrations. Our findings can be helpful in developing urban air quality control measures and management strategies, especially during high pollution episodes.

Urban climate and cardiovascular health: Focused on seasonal variation of urban temperature, relative humidity, and PM2.5 air pollution

Seasonal effects on subclinical cardiovascular functions (CVFs) are an important emerging health issue for people living in urban environment. The objectives of this study were to demonstrate the effects of seasonal variations of temperature, relative humidity, and PM2.5 air pollution on CVFs. A total of 86 office workers in Taipei City were recruited, their arterial pressure waveform was recorded by cuff sphygmomanometer using an oscillometric blood pressure (BP) device for CVFs assessment. Results of paried t-test with Bonferroni correction showed significantly increased systolic and diastolic BP (SBP, DBP), central end-systolic and diastolic BP (cSBP, cDBP) and systemic vascular resistance, but decreased heart rate (HR), stroke volume (SV), cardio output (CO), and cardiac index in winter compared with other seasons. After controlling for related confounding factors, SBP, DBP, cSBP, cDBP, LV dp/dt max, and brachial-ankle pulse wave velocity (baPWV) were negatively associated with, and SV was positively associated with seasonal temperature changes. Seasonal changes of air pollution in terms of PM2.5 were significantly positively associated with DBP and cDBP, as well as negatively associated with HR and CO. Seasonal changes of relative humidity were significantly negatively associated with DBP, and cDBP, as well as positively associated with HR, CO, and baPWV. This study provides evidence of greater susceptibility to cardiovascular events in winter compared with other seasons, with ambient temperature, relative humidity, and PM2.5 as the major factors of seasonal variation of CVFs.

Characterizing the effects of structural fires on fine particulate matter with a dense sensing network

Short-term increases in air pollution levels are linked to large adverse effects on health and productivity. However, existing regulatory monitoring systems lack the spatial or temporal resolution needed to capture localized events. This study uses a dense network of over 100 sensors, deployed across the city of Chicago, Illinois, to capture the spread of smoke from short-term structural fire events. Examining all large structural fires that occurred in the city over a year (N = 21), we characterize differences in PM[Formula: see text] concentrations downwind versus upwind of the fires. On average, we observed increases of up to 10.7 [Formula: see text]g/m[Formula: see text] (95% CI 5.7-15.7) for sensors within 2 km and up to 7.7 [Formula: see text]g/m[Formula: see text] (95% CI 3.4-12.0) for sensors 2-5 km downwind of fires. Statistically significant elevated concentrations were evident as far as 5 km downwind of the location of the fire and persisted over approximately 2 h on average. This work shows how low-cost sensors can provide insight on local and short-term pollution events, enabling regulators to provide timely warnings to vulnerable populations.

Impact analysis of meteorological variables on PM2.5 pollution in the most polluted cities in China

With the continuous promotion of urbanization in China, air pollution problems have become increasingly prominent in recent years. Various factors, such as emissions, meteorology, and physical and chemical reactions, jointly affect the severity of PM_2.5 pollution to a large extent. This study selected five meteorological variables (planetary boundary layer height (PBLH), wind speed (WS), temperature(T), water vapor mixing ratio(Q), and precipitation (PCP)) for perturbation, and 21 different scenarios were set up. In this study, the effects of changes in a single meteorological variable on the pollutants produced in the area were represented by subtracting the baseline scenario (i.e., without perturbation of meteorological variables) simulated in January 2017 separately from each post-disturbance scenario. The results showed that Handan (HD) has the highest annual mean PM_2.5 concentration of 85.75 μg/m³ in 2017, while all cities in study area exceeded the secondary concentration limit of urban atmospheric particulate matter. The correlation coefficient (R) between the simulation values of models and the actual monitoring values ranges from 0.41 to 0.74, indicating good model performance and acceptable simulation errors. PBLH (±10%-±20%), WS(±10%-±20%), and PCP(±10%-±20%) all showed a single adverse effect among the five meteorological variables, meaning that a reduction in these three factors led to an increase in PM_2.5 concentrations. However, T (±1 K-±1.5 K) and Q (±10%-±20%) could indicate a positive impact under certain conditions. From the sensitivity calculations of single meteorological variables, it is clear that WS, PBLH, and PCP show a highly linear trend in all cities at the 0.01 level of significance. The hypothesis that T changes linearly in 10 cities in the study area is valid, while for Q, the hypothesis that Q changes linearly only occurs in Shijiazhuang and Baoding. When different meteorological variables are disturbed, there are significant spatial differences in the main affected areas of PM_2.5 concentrations. By discussing the impact of meteorological variable disturbance on air quality in critically polluted cities in China, this study identified the meteorological variables that can substantially affect PM_2.5 concentration. The more complex T and Q should be considered when formulating relevant emission measures.

Influence of Temperature and Relative Humidity on PM2.5 Concentration over Delhi

The present study is an attempt to establish relationship between the concentrations of particulate matter especially (PM2.5) and background meteorological parameters over Delhi, India with the help of statistical and correlative analysis. This work presents the evaluation of air quality in three different locations of Delhi. These locations were selected to fulfil the characteristics as residential, industrial and background locations and performed the analysis for pre and post covid-19, i.e. for 2019 and 2021. The outcome of the study shows that the meteorological parameters have significant influence on the PM2.5 concentration. It was also found that it has a seasonality with low concentration in the monsoon season, moderate in the pre-monsoon season and high during the winters and post-monsoon seasons. However, the statistical and correlative study shows a negative relation with the temperature during the winter, pre-monsoon and post-monsoon and has a positive correlation during the monsoon season. Similarly, it also has been observed that the concentration of PM2.5 shows strong negative correlation with temperature during the high humid conditions, i.e. when the relative humidity is above 50%. However, a weak correlation with ambient temperature has been established during the low humidity condition, i.e. below 50%. The overall study showed that the highest PM2.5 pollution has been observed at residential location followed by industrial and background. The study also concluded that the seasonal meteorology has a complex role in the PM2.5 concentration of the selected areas.

A new strategy for risk assessment of PM2.5-bound elements by considering the influence of wind regimes

For regulatory purposes, air pollution has been reduced to management of air quality control regions (AQCR), by inventorying pollution sources and identifying the receptors significantly affected. However, beyond being source-dependent, particulate matter can be physically and chemically altered by factors and elements of climate during transport, as they act as local environmental constraints, indirectly modulating the adverse effects of particles on the environment and human health. This case study, at an industrial site in a Brazilian coastal city - Joinville, combines different methodologies to integrate atmospheric dynamics in a strategic risk assessment approach whereby the influence of different wind regimes on environmental and health risks of exposure to PM_2.5-bound elements, are analysed. Although Joinville AQCR has been prone to stagnation/recirculation events, distinctly different horizontal wind circulation patterns indicate two airsheds within the region. The two sampling sites mirrored these two conditions and as a result we report different PM_2.5 mass concentrations, chemical profiles, geo-accumulation, and ecological and human health risks. In addition, feedback mechanisms between the airsheds seem to aggravate the air quality and its effects even under good ventilation conditions. Recognizably, the risks associated with Co, Pb, Cu, Ni, Mn, and Zn loadings were extremely high for the environment as well as being the main contributors to elevated non-carcinogenic risks. Meanwhile, higher carcinogenic risks occurred during stagnation/recirculation conditions, with Cr as the major threat. These results highlight the importance of integrating local airshed characteristics into the risk assessment of PM_2.5-bound elements since they can aggravate air pollution leading to different risks at a granular scale. This new approach to risk assessment can be employed in any city's longer-term development plan since it provides public authorities with a strategic perspective on incorporating environmental constraints into urban growth planning and development zoning regulations.

Indoor air particles in research vessel from Shanghai to Antarctic: Characteristics, influencing factors, and potential controlling pathway

Despite millions of seafarers and passengers staying on ships each year, few studies have been conducted on the indoor air quality inside ship hulls. In this study, we investigated the levels and size distribution of indoor particulate matter during two cruises of the research vessel "Xuelong" from Shanghai to Antarctica. The results showed that the particle size less than 2.5 µm (PM_2.5), and particle size less than 10 µm (PM₁₀) concentrations in different rooms of the ship widely varied. We observed high particulate matter (PM) levels in some of the rooms. The mass concentration distribution was dominated by 1-4 µm particles, which may have been caused by the hygroscopic growth of fine particles. The dominant factors influencing PM concentrations were indoor temperature, relative humidity, and human activity. We quantified contributions of these factors to the levels of indoor particles using a generalized additive model. In clean rooms, the levels of indoor particles were controlled by temperature and relative humidity, whereas in polluted rooms, the levels of indoor particles were mainly influenced by temperature and human activity, which implied that controlling temperature and human activity would efficiently reduce the levels of indoor particles.

Personal PM2.5 Exposure Monitoring of Informal Cooking Vendors at Indoor and Outdoor Markets in Johannesburg, South Africa

Air pollutants of concern include particulate matter (PM) in fine size fractions. Thus far, a few studies have been conducted to study the adverse health effects of environmental and occupational air pollutants among informal vendors in big cities in South Africa. Informal vendors in these cities may experience higher exposure to road dust, cooking fumes, and air pollution. This exposure assessment was part of a health risk assessment study of vendors. The objective of this exposure assessment was to determine the differences between outdoor and indoor informal vendors' personal PM_2.5 exposures during trading hours. A walkthrough survey was conducted to map the homogeneous exposure groups (HEGs) at vendor markets for sampling purposes, and one market was selected from each of the three identified HEGs. Twenty-five informal cooked food vendors from both indoor (inside buildings) and outdoor (street or roadside vendors) markets in the inner city of Johannesburg, South Africa, participated in the study. HEG-1 were vendors from indoor stalls who used electricity and gas for cooking (10 vendors), HEG-2 was composed of informal outdoor vendors at a fenced site market who used open fire for cooking (10 vendors), and HEG-3 (5 vendors) were roadside vendors who used gas for cooking. Cooking vendors from outdoor markets recorded higher TWA concentrations than indoor market vendors. The vendors' PM_2.5 concentrations ranged from <0.01 mg/m³ to 0.77 mg/m³. The mean concentrations of PM_2.5 were found to be 0.12 mg/m³, and 0.18 mg/m³ for HEG-2, and HEG-3, respectively. HEG-2 recorded the highest PM_2.5 TWA concentrations, followed by HEG-3 and HEG-1. All concentrations were below the South African occupational exposure limit. The findings point to the need for further research into the health risks associated with outdoor cooking vendors, particularly those who utilize open fires.

Spatiotemporal distribution, trend, forecast, and influencing factors of transboundary and local air pollutants in Nagasaki Prefecture, Japan

The study of PM_2.5 and NO₂ has been emphasized in recent years due to their adverse effects on public health. To better understand these pollutants, many studies have researched the spatiotemporal distribution, trend, forecast, or influencing factors of these pollutants. However, rarely studies have combined these to generate a more holistic understanding that can be used to assess air pollution and implement more effective strategies. In this study, we analyze the spatiotemporal distribution, trend, forecast, and factors influencing PM_2.5 and NO₂ in Nagasaki Prefecture by using ordinary kriging, pearson's correlation, random forest, mann-kendall, auto-regressive integrated moving average and error trend and seasonal models. The results indicated that PM_2.5, due to its long-range transport properties, has a more substantial spatiotemporal variation and affects larger areas in comparison to NO₂, which is a local pollutant. Despite tri-national efforts, local regulations and legislation have been effective in reducing NO₂ concentration but less effective in reducing PM_2.5. This multi-method approach provides a holistic understanding of PM_2.5 and NO₂ pollution in Nagasaki prefecture, which can aid in implementing more effective pollution management strategies. It can also be implemented in other regions where studies have only focused on one of the aspects of air pollution and where a holistic understanding of air pollution is lacking.

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.

Spatial and Temporal Volatility of PM2.5, PM10 and PM10-Bound B[a]P Concentrations and Assessment of the Exposure of the Population of Silesia in 2018-2021

Air pollution both indoors and outdoors is a major cause of various diseases and premature deaths. Negative health effects are more frequently observed in a number of European countries characterized by significant pollution. In Poland, especially in Upper Silesia, the most serious problem is the high concentration of particulate matter (PM) and PM10-bound benzo[a]pyrene (B[a]P). The main source of these two pollutants is so-called "low emissions" associated with the burning of solid fuels mainly in domestic boilers and liquid fuels in road traffic. This study examined the variability in the PM and PM10-bound B[a]P concentrations and their relationships with meteorological parameters, i.e., atmospheric pressure, air temperature and wind speed, in 2018-2021 at 11 monitoring stations. In many Silesian cities, the average annual concentrations of PM10, PM2.5 and B[a]P were much higher than those recorded in other European countries. At each station, the average daily PM10 concentrations were exceeded on 12 to 126 days a year. Taking into account the WHO recommendation for PM2.5, the highest recorded average daily concentration exceeded the permissible level by almost 40 times. The same relationships were observed in all measurement years: PM10 concentrations were negatively correlated with air temperature (R = -0.386) and wind speed (R = -0.614). The highest concentrations were observed in the temperature range from -15 °C to -5 °C, when the wind speed did not exceed 0.5 m·s^-1. The calculated lifetime cancer risk (LCR) associated with the exposure to B[a]P in the Silesian Voivodeship suggested 30-429 cases per 1 million people in the heating season depending on the scenario used for the calculations (IRIS, EPA or WHO).

Impact of SARS-CoV-2 variants on mobility and air pollution in the United Kingdom

During the two years following the first case of COVID-19 in the United Kingdom, cycles of social restrictions were imposed to control the spread of the virus. These measures curtailed social contact and halted commercial and recreational activities affecting levels of air pollutants. As society adapted, restrictions eased and pollution gradually returned to baseline levels. However, resurgence in COVID-19 cases from new variants created a protracted and challenging path back to 'normality'. In this study, we retrospectively look back at the two years of COVID-19 and its prevalent variants, and examine the government response and its impact on mobility and air pollution. Results from a peak detection algorithm show peak events in mobility and COVID-19 deaths during variants periods decreased significantly from the wildtype COVID-19, despite the high contagiousness of these variants. Pollution levels remained below baseline with periods of significant increase for O₃, while NO₂ levels remained depleted, likely as a result of reduced traffic congestion as home office schemes have been maintained. Our findings suggest mobility and pollution return to baseline levels as immunity to COVID-19 increases.

PM2.5 exceedances and source appointment as inputs for an early warning system

Between June 2018 and April 2019, a sampling campaign was carried out to collect PM_2.5, monitoring meteorological parameters and anthropogenic events in the Sartenejas Valley, Venezuela. We develop a logistic model for PM_2.5 exceedances (≥ 12.5 µg m^-3). Source appointment was done using elemental composition and morphology of PM by scanning electron microscopy coupled with energy dispersive spectroscopy (SEM-EDS). A proposal of an early warning system (EWS) for PM pollution episodes is presented. The logistic model has a holistic success rate of 94%, with forest fires and motor vehicle flows as significant variables. Source appointment analysis by occurrence of events showed that samples with higher concentrations of PM had carbon-rich particles and traces of K associated with biomass burning, as well as aluminosilicates and metallic elements associated with resuspension of soil dust by motor-vehicles. Quantitative source appointment analysis showed that soil dust, garbage burning/marine aerosols and wildfires are three majority sources of PM. An EWS for PM pollution episodes around the Sartenejas Valley is proposed considering the variables and elements mentioned.

Effect of PM2.5 Levels on ED Visits for Respiratory Causes in a Greek Semi-Urban Area

Fine particulate matter that have a diameter of <2.5 μm (PM2.5) are an important factor of anthropogenic pollution since they are associated with the development of acute respiratory illnesses. The aim of this prospective study is to examine the correlation between PM2.5 levels in the semi-urban city of Volos and Emergency Department (ED) visits for respiratory causes. ED visits from patients with asthma, pneumonia and upper respiratory infection (URI) were recorded during a one-year period. The 24 h PM2.5 pollution data were collected in a prospective manner by using twelve fully automated air quality monitoring stations. PM2.5 levels exceeded the daily limit during 48.6% of the study period, with the mean PM2.5 concentration being 30.03 ± 17.47 μg/m3. PM2.5 levels were significantly higher during winter. When PM2.5 levels were beyond the daily limit, there was a statistically significant increase in respiratory-related ED visits (1.77 vs. 2.22 visits per day; p: 0.018). PM2.5 levels were also statistically significantly related to the number of URI-related ED visits (0.71 vs. 0.99 visits/day; p = 0.01). The temperature was negatively correlated with ED visits (r: −0.21; p < 0.001) and age was found to be positively correlated with ED visits (r: 0.69; p < 0.001), while no statistically significant correlation was found concerning humidity (r: 0.03; p = 0.58). In conclusion, PM2.5 levels had a significant effect on ED visits for respiratory causes in the city of Volos.

Spatiotemporal impact of COVID-19 on Taiwan air quality in the absence of a lockdown: Influence of urban public transportation use and meteorological conditions

The unprecedented outbreak of COVID-19 significantly improved the atmospheric environment for lockdown-imposed regions; however, scant evidence exists on its impacts on regions without lockdown. A novel research framework is proposed to evaluate the long-term monthly spatiotemporal impact of COVID-19 on Taiwan air quality through different statistical analyses, including geostatistical analysis, change detection analysis and identification of nonattainment pollutant occurrence between the average mean air pollutant concentrations from 2018-2019 and 2020, considering both meteorological and public transportation impacts. Contrary to lockdown-imposed regions, insignificant or worsened air quality conditions were observed at the beginning of COVID-19, but a delayed improvement occurred after April in Taiwan. The annual mean concentrations of PM₁₀, PM_2.5, SO₂, NO₂, CO and O₃ in 2020 were reduced by 24%, 18%, 15%, 9.6%, 7.4% and 1.3%, respectively (relative to 2018-2019), and the overall occurrence frequency of nonattainment air pollutants declined by over 30%. Backward stepwise regression models for each air pollutant were successfully constructed utilizing 12 meteorological parameters (R² > 0.8 except for SO₂) to simulate the meteorological normalized business-as-usual concentration. The hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model simulated the fate of air pollutants (e.g., local emissions or transboundary pollution) for anomalous months. The changes in different public transportation usage volumes (e.g., roadway, railway, air, and waterway) moderately reduced air pollution, particularly CO and NO₂. Reduced public transportation use had a more significant impact than meteorology on air quality improvement in Taiwan, highlighting the importance of proper public transportation management for air pollution control and paving a new path for sustainable air quality management even in the absence of a lockdown.

Spatiotemporal variations of PM2.5 pollution and its dynamic relationships with meteorological conditions in Beijing-Tianjin-Hebei region

Identifying the effects of meteorological conditions on PM_2.5 pollution is of great significance to explore methods to reduce atmospheric pollution. This study attempts to analyze the spatiotemporal variations of PM_2.5 pollution and its dynamic nexus with meteorological factors in the Beijing-Tianjin-Hebei (BTH) region from 2015 to 2020 using standard deviation ellipse (SDE) and panel vector autoregressive (PVAR) model. The results indicate that: (1) In 2015-2020, PM_2.5 pollution decreased significantly, indicating air pollution control policies in China have taken effect; Also, it showed a cumulative effect, or there was the path dependence of air pollution. (2) PM_2.5 pollution presented a distribution pattern from northeast to southwest, while the directionality of air pollution has weakened. Based on SDE, PM_2.5 pollution in Cangzhou can reflect the average level in the BTH; (3) Meteorological conditions exhibited a lagged and sustained effect on PM_2.5 pollution. Specifically, the effects of meteorological factors on PM_2.5 presented disequilibrium over time. In the long run, precipitation and temperature mainly showed negative impacts on PM_2.5 pollution, while wind speed, relative humidity and sunshine duration aggravated PM_2.5 pollution in the BTH. This study contributes to extending the study on the spatiotemporal evolution of PM_2.5 pollution and its links with meteorological conditions.

Machine Learning-Based Approach Using Open Data to Estimate PM2.5 over Europe

Air pollution is currently considered one of the most serious problems facing humans. Fine particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) is a very harmful air pollutant that is linked with many diseases. In this study, we created a machine learning-based scheme to estimate PM2.5 using various open data such as satellite remote sensing, meteorological data, and land variables to increase the limited spatial coverage provided by ground-monitors. A space-time extremely randomised trees model was used to estimate PM2.5 concentrations over Europe, this model achieved good results with an out-of-sample cross-validated R2 of 0.69, RMSE of 5 μg/m3, and MAE of 3.3 μg/m3. The outcome of this study is a daily full coverage PM2.5 dataset with 1 km spatial resolution for the three-year period of 2018–2020. We found that air quality improved throughout the study period over all countries in Europe. In addition, we compared PM2.5 levels during the COVID-19 lockdown during the months March–June with the average of the previous 4 months and the following 4 months. We found that this lockdown had a positive effect on air quality in most parts of the study area except for the United Kingdom, Ireland, north of France, and south of Italy. This is the first study that depends only on open data and covers the whole of Europe with high spatial and temporal resolutions. The reconstructed dataset will be published under free and open license and can be used in future air quality studies.

Exposure models for particulate matter elemental concentrations in Southern California

Due to a scarcity of routine monitoring of speciated particulate matter (PM), there has been limited capability to develop exposure models that robustly estimate component-specific concentrations. This paper presents the largest such study conducted in a single urban area. Using samples that were collected at 220 locations over two seasons, quasi-ultrafine (PM_0.2), accumulation mode fine (PM_0.2-2.5), and coarse (PM_2.5-10) particulate matter concentrations were used to develop spatiotemporal regression, machine learning models that enabled predictions of 24 elemental components in eight Southern California communities. We used supervised variable selection of over 150 variables, largely from publicly available sources, including meteorological, roadway and traffic characteristics, land use, and dispersion model estimates of traffic emissions. PM components that have high oxidative potential (and potentially large health effects) or are otherwise important markers for major PM sources were the primary focus. We present results for copper, iron, and zinc (as non-tailpipe vehicle emissions); elemental carbon (diesel emissions); vanadium (ship emissions); calcium (soil dust); and sodium (sea salt). Spatiotemporal linear regression models with 17 to 36 predictor variables including meteorology; distance to different classifications of roads; intersections and off ramps within a given buffer distance; truck and vehicle traffic volumes; and near-roadway dispersion model estimates produced superior predictions over the machine learning approaches (cross validation R-squares ranged from 0.76 to 0.92). Our models are easily interpretable and appear to have more effectively captured spatial gradients in the metallic portion of PM than other comparably large studies, particularly near roadways for the non-tailpipe emissions. Furthermore, we demonstrated the importance of including spatiotemporally resolved meteorology in our models as it helped to provide key insights into spatial patterns and allowed us to make temporal predictions.

Temporal variability of PM2.5 and its possible sources at the tropical megacity, Bengaluru, India

The mass concentrations of PM_2.5 were measured at a tropical megacity, Bengaluru, India, for the year 2015. The mean mass concentrations showed large fluctuations on day to day basis with values less than the Indian National Ambient Air Quality Standard (INAAQS) of 60 µg m^-3. The observed annual mean mass concentration of 28 ± 11 µg m^-3 is also within the INAAQS value of 40 µg m^-3. The diurnal trend of PM_2.5 concentration showed bimodal distribution, with the primary peak in the morning and the secondary one during the late evening hours. The timing of the peaks matched with rush traffic hours. Strong seasonality is observed in the diurnal concentration of PM_2.5 with the highest value during winter (50 ± 22 µg m^-3) and the lowest of (11 ± 5 µg m^-3) in the monsoon. The weekend PM_2.5 mass concentrations were less than those on the weekdays up to a maximum of 100%. The decrease in PM_2.5 mass concentration was also observed on the day of the strike when many busses were off the road. Vehicular traffic is suggested as one of the primary contributors of PM_2.5 in this region. The health risk assessment in this study, points to ischemic heart disease as the primary cause of PM_2.5-induced death.

Regional Differences, Distribution Dynamics, and Convergence of Air Quality in Urban Agglomerations in China

The urban agglomeration (UA), with a high concentration of population and economy, represents an area with grievous air pollution. It is vital to examine the regional differences, distribution dynamics, and air quality convergence in UAs for sustainable development. In this study, we measured the air quality of ten UAs in China through the Air Quality Index (AQI). We analyzed regional differences, distribution dynamics, and convergence using Dagum’s decomposition of the Gini coefficient, kernel density estimation, and the convergence model. We found that: the AQI of China’s UAs shows a downward trend, and the index is higher in northern UAs than in southern UAs; the differences in air quality within UAs are not significant, but there is a gap between them; the overall difference in air quality tends to decrease, and regional differences in air quality are the primary contributor to the overall difference; the overall distribution and the distribution of each UA move rightward; the distribution pattern, ductility, and polarization characteristics are different, indicating that the air quality has improved and is differentiated between UAs; except for the Guanzhong Plain, the overall UA and each UA have obvious σ convergence characteristics, and each UA presents prominent absolute β convergence, conditional β convergence, and club convergence.

Assessment of Fine Particulate Matter for Port City of Eastern Peninsular India Using Gradient Boosting Machine Learning Model

An assessment and prediction of PM2.5 for a port city of eastern peninsular India is presented. Fifteen machine learning (ML) regression models were trained, tested and implemented to predict the PM2.5 concentration. The predicting ability of regression models was validated using air pollutants and meteorological parameters as input variables collected from sites located at Visakhapatnam, a port city on the eastern side of peninsular India, for the assessment period 2018–2019. Highly correlated air pollutants and meteorological parameters with PM2.5 concentration were evaluated and presented during the period under study. It was found that the CatBoost regression model outperformed all other employed regression models in predicting PM2.5 concentration with an R2 score (coefficient of determination) of 0.81, median absolute error (MedAE) of 6.95 µg/m3, mean absolute percentage error (MAPE) of 0.29, root mean square error (RMSE) of 11.42 µg/m3 and mean absolute error (MAE) of 9.07 µg/m3. High PM2.5 concentration prediction results in contrast to Indian standards were also presented. In depth seasonal assessments of PM2.5 concentration were presented, to show variance in PM2.5 concentration during dominant seasons.

Structural Differences of PM2.5 Spatial Correlation Networks in Ten Metropolitan Areas of China

The cross-impact of environmental pollution among cities has been reported in more research works recently. To implement the coordinated control of environmental pollution, it is necessary to explore the structural characteristics and influencing factors of the PM2.5 spatial correlation network from the perspective of the metropolitan area. This paper utilized the gravity model to construct the PM2.5 spatial correlation network of ten metropolitan areas in China from 2019 to 2020. After analyzing the overall characteristics and node characteristics of each spatial correlation network based on the social network analysis (SNA) method, the quadratic assignment procedure (QAP) regression analysis method was used to explore the influence mechanism of each driving factor. Patent granted differences, as a new indicator, were also considered during the above. The results showed that: (1) In the overall network characteristics, the network density of Chengdu and the other three metropolitan areas displayed a downward trend in two years, and the network density of Wuhan and Chengdu was the lowest. The network density and network grade of Hangzhou and the other four metropolitan areas were high and stable, and the network structure of each metropolitan area was unstable. (2) From the perspective of the node characteristics, the PM2.5 spatial correlation network all performed trends of centralization and marginalization. Beijing-Tianjin-Hebei and South Central Liaoning were “multi-core” metropolitan areas, and the other eight were “single-core” metropolitan areas. (3) The analysis results of QAP regression illustrated that the top three influencing factors of the six metropolitan areas were geographical locational relationship, the secondary industrial proportion differences, respectively, and patent granted differences, and the other metropolitan areas had no dominant influencing factors.

Analysis of PM2.5 and Meteorological Variables Using Enhanced Geospatial Techniques in Developing Countries: A Case Study of Cartagena de Indias City (Colombia)

The dispersion of air pollutants and the spatial representation of meteorological variables are subject to complex atmospheric local parameters. To reduce the impact of particulate matter (PM2.5) on human health, it is of great significance to know its concentration at high spatial resolution. In order to monitor its effects on an exposed population, geostatistical analysis offers great potential to obtain high-quality spatial representation mapping of PM2.5 and meteorological variables. The purpose of this study was to define the optimal spatial representation of PM2.5, relative humidity, temperature and wind speed in the urban district in Cartagena, Colombia. The lack of data due to the scarcity of stations called for an ad hoc methodology, which included the interpolation implementing an ordinary kriging (OK) model, which was fed by data obtained through the inverse distance weighting (IDW) model. To consider wind effects, empirical Bayesian kriging regression prediction (EBK) was implemented. The application of these interpolation methods clarified the areas across the city that exceed the recommended limits of PM2.5 concentrations (Zona Franca, Base Naval and Centro district), and described in a continuous way, on the surface, three main weather variables. Positive correlations were obtained for relative humidity (R2 of 0.47), wind speed (R2 of 0.59) and temperature (R2 of 0.64).

The effect of the revision and implementation for environmental protection law on ambient air quality in China

An unescapable fact is that air pollution has been a problem affecting residents' health and daily life. The Chinese government has been adopting measures to improve air quality for decades. The revise of Environmental Protection Law (the New Law hereafter) enforced in 2015 is one of them. The New Law encourages participations of multiple actors in environmental protection and aggressive punishments violations, playing the central role in the Chinese environmental law system. In order to understand its impacts, we employ the panel data analysis controlling city and month fixed terms to evaluate the effects of the New Law on air quality in 70 cities in China. Furthermore, we combine difference-in-differences (DID) to investigate the time variance of the effect. We find that the implementation of the New Law correlates with reduction of PM_2.5, SO₂ concentrations and Air Quality Comprehensive Index (AQCI). The effect is non-linear, reducing over time, especially on NO₂ concentration and AQCI. In our model, one document reduces NO₂ concentration and AQCI by 1.99 μg/m³ and 0.26 points, and the effects decay by 0.93 μg/m3 and 0.16 every year separately. The results indicate the effectiveness of the New Law, while at the same time, China experiences symbolic implementations from local authorizations resulted from environmental decentralization, ambiguous policy statements and interest conflicts.

Factors Influencing PM2.5 Concentrations in the Beijing–Tianjin–Hebei Urban Agglomeration Using a Geographical and Temporal Weighted Regression Model

Air pollution is the environmental issue of greatest concern in China, especially the PM2.5 pollution in the Beijing–Tianjin–Hebei urban agglomeration (BTHUA). Based on sustainable development, it is of interest to study the spatiotemporal distribution of PM2.5 and its influencing mechanisms. This study reveals the temporal evolution and spatial clustering characteristic of PM2.5 pollution from 2015 to 2019, and quantifies the drivers of its natural and socioeconomic factors on it by using a geographical temporal weighted regression model. Results show that PM2.5 concentrations reached their highest level in 2015 before decreasing in the following years. The monthly averages all present a U-shaped change trend. Relative to the traditional high concentrations in the northern part of the BTHUA domain in 2015, the gap in pollution between the north and south has reduced since 2018. The obvious spatial heterogeneity was demonstrated in both the strength and direction of the variables. This study may help identify reasons for high PM2.5 concentrations and suggest appropriate targeted control and prevention measures.

Interpolation-Based Fusion of Sentinel-5P, SRTM, and Regulatory-Grade Ground Stations Data for Producing Spatially Continuous Maps of PM2.5 Concentrations Nationwide over Thailand

Atmospheric pollution has recently drawn significant attention due to its proven adverse effects on public health and the environment. This concern has been aggravated specifically in Southeast Asia due to increasing vehicular use, industrial activity, and agricultural burning practices. Consequently, elevated PM2.5 concentrations have become a matter of intervention for national authorities who have addressed the needs of monitoring air pollution by operating ground stations. However, their spatial coverage is limited and the installation and maintenance are costly. Therefore, alternative approaches are necessary at national and regional scales. In the current paper, we investigated interpolation models to fuse PM2.5 measurements from ground stations and satellite data in an attempt to produce spatially continuous maps of PM2.5 nationwide over Thailand. Four approaches are compared, namely the inverse distance weighted (IDW), ordinary kriging (OK), random forest (RF), and random forest combined with OK (RFK) leveraging on the NO2, SO2, CO, HCHO, AI, and O3 products from the Sentinel-5P satellite, regulatory-grade ground PM2.5 measurements, and topographic parameters. The results suggest that RFK is the most robust, especially when the pollution levels are moderate or extreme, achieving an RMSE value of 7.11 μg/m3 and an R2 value of 0.77 during a 10-day long period in February, and an RMSE of 10.77 μg/m3 and R2 and 0.91 during the entire month of March. The proposed approach can be adopted operationally and expanded by leveraging regulatory-grade stations, low-cost sensors, as well as upcoming satellite missions such as the GEMS and the Sentinel-5.

Assessment of temporal shifting of PM2.5, lockdown effect, and influences of seasonal meteorological factors over the fastest-growing megacity, Dhaka

Dhaka is subjected to high pollution levels throughout the year, holding some relatively high amounts of pollution readings, making its air unhealthy to breathe. The study examined hourly, shifting, seasonal fluctuations in particulate matter (PM_2.5), the effects of seasonal meteorological variables, and the lockdown effect over the megacity of Dhaka from 2019 to 2021 using data from AirNow. The results indicate the daily average PM_2.5 concentration between 2019 and 2021 was 112.49 µg/m³, about four times higher than the WHO limit and two times higher than the Bangladesh standard. Daily PM_2.5 concentrations was high during morning and evening pick-up hours, reaching a maximum hourly concentration of 472.9 µg/m³ in February 2020. The maximum average PM_2.5 concentration was 211.23 µg/m³ in March 2021 (winter season), and the lowest average was 27.58 µg/m³ in August 2020 (rainy season). The Pearson correlation coefficient (r) between the PM_2.5 and meteorological variables were inverse with rainfall (− 0.62), temperature (− 0.73), humidity (− 0.82), but positive with wind (0.09). Daily average Air Quality Index (AQI) concentrations improved from 108.53 to 67.99 µg/m³ during the lockdown period. Finally, the study recommended many mitigation strategies that might assist accountable authorities in lowering the number of life-threatening components in the air.

Spatio-temporal modeling of PM2.5 risk mapping using three machine learning algorithms

Urban air pollution is one of the most critical issues that affect the environment, community health, economy, and management of urban areas. From a public health perspective, PM_2.5 is one of the primary air pollutants, especially in Tehran's metropolis. Owing to the different patterns of PM_2.5 in different seasons, Spatio-temporal modeling and identification of high-risk areas to reduce its effects seems necessary. The purpose of this study was Spatio-temporal modeling and preparation of PM_2.5 risk mapping using three machine learning algorithms (random forest (RF), AdaBoost, and stochastic gradient descent (SGD)) in the metropolis of Tehran, Iran. Therefore, in the first step, to prepare the dependent variable data, the PM_2.5 average was used for the four seasons of spring, summer, autumn, and winter. Then, using remote sensing (RS) and a geographic information system (GIS), independent data such as temperature, maximum temperature, minimum temperature, wind speed, rainfall, humidity, normalized difference vegetation index (NDVI), population density, street density, and distance to industrial centers were prepared as a seasonal average. To Spatio-temporal modeling using machine learning algorithms, 70% of the data were used for training and 30% for validation. The frequency ratio (FR) model was used as input to machine learning algorithms to calculate the spatial relationship between PM_2.5 and the effective parameters. Finally, Spatio-temporal modeling and PM_2.5 risk mapping were performed using three machine learning algorithms. The receiver operating characteristic (ROC) area under the curve (AUC) results showed that the RF algorithm had the greatest modeling accuracy, with values of 0.926, 0.94, 0.949, and 0.949 for spring, summer, autumn, and winter, respectively. According to the RF model, the most important variable in spring and autumn was NDVI. Temperature and distance to industrial centers were the most important variables in the summer and winter, respectively. The results showed that autumn, winter, summer, and spring had the highest risk of PM_2.5, respectively.

Forecasting PM2.5 concentration using artificial neural network and its health effects in Ahvaz, Iran

The main objective of the present study was to predict the associated health endpoint of PM_2.5 using an artificial neural network (ANN). The neural network used in this work contains a hidden layer with 27 neurons, an input layer with 8 parameters, and an output layer. First, the artificial neural network was implemented with 80% of data for training then with 90% of data for training. The value of R for the data validation of these two networks was 0.80 and 0.83 respectively. The World Health Organization AirQ ⁺ software was utilized for assessing Health effects of PM_2.5 levels. The mean PM_2.5 over the 9-year study period was 63.27(μg/m³), about six times higher than the WHO guideline. However, the PM_2.5 concentration in the last year decreased by about 25% compared to the first year, which is statistically significant (P-value = 0.0048). This reduced pollutant concentration led to a decrease in the number of deaths from 1785 in 2008 to 1059 in 2016. Moreover, a positive correlation was found between PM_2.5 concentration and temperature and wind speed. Considering the importance of predicting PM_2.5 concentration for accurate and timely decisions as well as the accuracy of the artificial neural network used in this study, the artificial neural network can be utilized as an effective instrument to reduce health and economic effects.

Sensitivity of PM2.5 and O3 pollution episodes to meteorological factors over the North China Plain

The Comprehensive Air-quality Model with extensions (CAMx) was used to explore the sensitivity of PM_2.5 and O₃ concentrations to four selected meteorological factors: wind speed, temperature, water vapor mixing ratio (Q), and planetary boundary layer height (PBLH) during two pollution episodes over the North China Plain (NCP). We also investigated the impact pathways of different meteorological factors on the formation of PM_2.5 and O_3. It is found that PM_2.5 was more sensitive to the selected meteorological factors in the southeastern NCP, where high anthropogenic emissions and severe air pollution occur. Large variations were observed along the Taihang Mountains, where the height of the terrain changes dramatically. The sensitivity of O₃ to wind speed, PBLH, temperature, and Q was mainly determined by the inhibition effects of PM_2.5 in winter, while in summer, the complex chemical reactions were dominant. Significant diurnal variations of process analysis (PA) results were observed under various meteorological conditions. Higher temperature generally enhance heterogeneous chemistry and transport of NO₃^- through the top boundary layer during night-time in winter, however, in summer, the heterogeneous chemistry of NO₃^- and NH₄⁺ during daytime were the major pathways to the increased PM_2.5 due to increased temperature. Moreover, temperature alter PM_2.5 concentrations through affecting vertical diffusivity and relative humidity, and alter O₃ concentrations by affecting the gas phase chemistry and mass fluxes through the top boundary layer. Q mainly affects the rate of chemical reactions of PM_2.5 and O₃. The different impact pathways suggest that it is essential to consider variations in meteorological factors, in addition to the direct impacts of wind speed and PBLH, more attention should be paid to the complex impacts of temperature and Q, when developing emission control strategies.

Source apportionment of fine atmospheric particles using positive matrix factorization in Pretoria, South Africa

In Pretoria South Africa, we looked into the origins of fine particulate matter (PM_2.5), based on 1-year sampling campaign carried out between April 18, 2017, and April 17, 2018. The average PM_2.5 concentration was 21.1 ± 15.0 µg/m³ (range 0.7-66.8 µg/m³), with winter being the highest and summer being the lowest. The XEPOS 5 energy dispersive X-ray fluorescence (EDXRF) spectroscopy was used for elemental analysis, and the US EPA PMF 5.0 program was used for source apportionment. The sources identified include fossil fuel combustion, soil dust, secondary sulphur, vehicle exhaust, road traffic, base metal/pyrometallurgical, and coal burning. Coal burning and secondary sulphur were significantly higher in winter and contributed more than 50% of PM_2.5 sources. The HYSPLIT model was used to calculate the air mass trajectories (version 4.9). During the 1-year research cycle, five transportation clusters were established: North Limpopo (NLP), Eastern Inland (EI), Short-Indian Ocean (SIO), Long-Indian Ocean (LIO), and South Westerly-Atlantic Ocean (SWA). Local and transboundary origin accounted for 85%, while 15% were long-range transport. Due to various anthropogenic activities such as biomass burning and coal mining, NLP clusters were the key source of emissions adding to the city's PM rate. In Pretoria, the main possible source regions of PM_2.5 were discovered to be NLP and EI. Effective control strategies designed at reducing secondary sulphur, coal burning, and fossil fuel combustion emissions at Southern African level and local combustion sources would be an important measure to combat the reduction of ambient PM_2.5 pollution in Pretoria.

PM2.5 Pollutant Concentrations in Greenspaces of Nanjing Are High but Can Be Lowered with Environmental Planning

Small-scale greenspaces in high-density central urban districts serve as important outdoor activity spaces for the surrounding residents, especially the elderly. This study selects six small-scale, popular greenspaces with distinct characteristics that are jointly situated along the same main urban artery in a high-density central urban district. Field investigations and questionnaires are conducted and combined with statistical analyses, to explore the spatial-temporal distribution and influencing factors of PM2.5 concentrations in these greenspaces. The study finds that the air quality conditions in the sites are non-ideal, and this has potential negative impacts on the health of the elderly visitors. Moreover, the difference values of PM2.5 concentrations' spatial-temporal distributions are significantly affected by vehicle-related emissions, which have significant temporal characteristics. PM2.5 concentration is strongly correlated with percentage of green coverage (R = 0.82, p < 0.05), degree of airflow (R = -0.83, p < 0.05), humidity and comfort level (R = 0.54, p < 0.01 and R = -0.40, p < 0.01 respectively). Meanwhile, the sites' "sky view factor" is strongly correlated with degree of airflow (R = 0.82, p < 0.05), and the comfort level plays an indirect role in the process of PM2.5 affecting crowd activities. Based on this analysis, an optimal set of index ranges for greenspace elements which are correlated with the best reduction in PM2.5 concentrations is derived. As such, this research reveals the technical methods to best reduce their concentrations and provides a basis and reference for improving the quality of small-scale greenspaces in high-density urban districts for the benefit of healthy aging.

A National-Scale 1-km Resolution PM2.5 Estimation Model over Japan Using MAIAC AOD and a Two-Stage Random Forest Model

Satellite-based models for estimating concentrations of particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) have seldom been developed in islands with complex topography over the monsoon area, where the transport of PM2.5 is influenced by both the synoptic-scale winds and local-scale circulations compared with the continental regions. We validated Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) with ground observations in Japan and developed a 1-km-resolution national-scale model between 2011 and 2016 to estimate daily PM2.5 concentrations. A two-stage random forest model integrating MAIAC AOD with meteorological variables and land use data was applied to develop the model. The first-stage random forest model was used to impute the missing AOD values. The second-stage random forest model was then utilised to estimate ground PM2.5 concentrations. Ten-fold cross-validation was performed to evaluate the model performance. There was good consistency between MAIAC AOD and ground truth in Japan (correlation coefficient = 0.82 and 74.62% of data falling within the expected error). For model training, the model showed a training coefficient of determination (R2) of 0.98 and a root mean square error (RMSE) of 1.22 μg/m3. For the 10-fold cross-validation, the cross-validation R2 and RMSE of the model were 0.86 and 3.02 μg/m3, respectively. A subsite validation was used to validate the model at the grids overlapping with the AERONET sites, and the model performance was excellent at these sites with a validation R2 (RMSE) of 0.94 (1.78 μg/m3). Additionally, the model performance increased as increased AOD coverage. The top-ten important predictors for estimating ground PM2.5 concentrations were day of the year, temperature, AOD, relative humidity, 10-m-height zonal wind, 10-m-height meridional wind, boundary layer height, precipitation, surface pressure, and population density. MAIAC AOD showed high retrieval accuracy in Japan. The performance of the satellite-based model was excellent, which showed that PM2.5 estimates derived from the model were reliable and accurate. These estimates can be used to assess both the short-term and long-term effects of PM2.5 on health outcomes in epidemiological studies.

The Adverse Impact of Air Pollution on China’s Economic Growth

This study empirically evaluates the impact of air pollution on China’s economic growth, based on a province-level sample for the period 2002–2017. Air pollution is measured by the concentration of fine particulate matter (PM2.5), and economic growth is measured by the annual growth rate of gross domestic product (GDP) per capita. A panel data fixed-effects regression model is built, and the instrumental variables estimation method is utilized for quantitative analyses. The study reports a significant negative impact of air pollution on the macroeconomic growth of China. According to our instrumental variables estimation, holding other factors constant, if the concentration of PM2.5 increases by 1%, then the GDP per capita growth rate will decline by 0.05818 percentage points. In addition, it is found that the adverse effect of atmospheric pollution is heterogeneous across different regions. The effect is stronger in the eastern region and in provinces with smaller state-owned enterprise shares, fewer governmental expenditures for public health services, and fewer medical resources. The study results reveal that air pollution poses a substantial threat to the sustainable economic growth of China. Taking actions to abate air pollution will generate great economic benefits, especially for those regions which are heavily damaged by pollution.

PM2.5 Pollution Strongly Predicted COVID-19 Incidence in Four High-Polluted Urbanized Italian Cities during the Pre-Lockdown and Lockdown Periods

BACKGROUND

The coronavirus disease in 2019 (COVID-19) heavily hit Italy, one of Europe's most polluted countries. The extent to which PM pollution contributed to COVID-19 diffusion is needing further clarification. We aimed to investigate the particular matter (PM) pollution and its correlation with COVID-19 incidence across four Italian cities: Milan, Rome, Naples, and Salerno, during the pre-lockdown and lockdown periods.

METHODS

We performed a comparative analysis followed by correlation and regression analyses of the daily average PM10, PM2.5 concentrations, and COVID-19 incidence across four cities from 1 January 2020 to 8 April 2020, adjusting for several factors, taking a two-week time lag into account.

RESULTS

Milan had significantly higher average daily PM10 and PM2.5 levels than Rome, Naples, and Salerno. Rome, Naples, and Salerno maintained safe PM10 levels. The daily PM2.5 levels exceeded the legislative standards in all cities during the entire period. PM2.5 pollution was related to COVID-19 incidence. The PM2.5 levels and sampling rate were strong predictors of COVID-19 incidence during the pre-lockdown period. The PM2.5 levels, population's age, and density strongly predicted COVID-19 incidence during lockdown.

CONCLUSIONS

Italy serves as a noteworthy paradigm illustrating that PM2.5 pollution impacts COVID-19 spread. Even in lockdown, PM2.5 levels negatively impacted COVID-19 incidence.

Overviewing the air quality models on air pollution in Sichuan Basin, China

Most developing countries in the world face the common challenges of reducing air pollution and advancing the process of sustainable development, especially in China. Air pollution research is a complex system and one of the main methods is through numerical simulation. The air quality model is an important technical method, it allows researchers to better analyze air pollutants in different regions. In addition, the SCB is a high-humidity and foggy area, and the concentration of atmospheric pollutants is always high. However, research on this region, one of the four most polluted regions in China, is still lacking. Reviewing the application of air quality models in the SCB air pollution has not been reported thoroughly. To fill these gaps, this review provides a comprehensive narration about i) The status of air pollution in SCB; ii) The application of air quality models in SCB; iii) The problems and application prospects of air quality models in the research of air pollution. This paper may provide a theoretical reference for the prevention and control of air pollution in the SCB and other heavily polluted areas in China and give some¹inspirations for air pollution forecast in other countries with complex terrain.

Effect of urban underlying surface on PM2.5 vertical distribution based on UAV in Xi'an, China

Fine particulate matter (PM_2.5) has become a significant issue of ecological environment. However, few studies have explored the vertical distribution of PM_2.5 in cities. The objectives of this paper are to reveal the vertical distribution regular pattern of PM_2.5 over urban underlying surfaces near the ground with a hexacopter-type unmanned aerial vehicle (UAV) in winter. Results showed that the maximum vertical gradient of PM_2.5 near the ground was typically the greatest in the morning as the stable atmospheric conditions. Moreover, regression model illustrated that relative humidity had the greatest impact on the vertical profile of PM_2.5 compared to air temperature and altitude as hygroscopic of PM_2.5 aerosols. Curve model shown that vertical profile of PM_2.5 over the surfaces of water and green space first increased slowly and then declined, besides, the highest concentration inflection of PM_2.5 above the water body (23.7 m) is higher than the green space (14.3 m). Thus, suggesting residents living vertical of 10-30 m from the ground around large water bodies and green spaces should not open windows for ventilation in the morning. Therefore, this study provides insights into the vertical distributions of PM_2.5 over different underlying surfaces and should be of reference value to urban planners for designing urban spaces to optimize atmosphere environment to provide a healthy living environment.

Ambient Levels, Emission Sources and Health Effect of PM2.5-Bound Carbonaceous Particles and Polycyclic Aromatic Hydrocarbons in the City of Kuala Lumpur, Malaysia

With increasing interest in understanding the contribution of secondary organic aerosol (SOA) to particulate air pollution in urban areas, an exploratory study was carried out to determine levels of carbonaceous aerosols and polycyclic aromatic hydrocarbons (PAHs) in the city of Kuala Lumpur, Malaysia. PM2.5 samples were collected using a high-volume sampler for 24 h in several areas in Kuala Lumpur during the north-easterly monsoon from January to March 2019. Samples were analyzed for water-soluble organic carbon (WSOC), organic carbon (OC), and elemental carbon (EC). Secondary organic carbon (SOC) in PM2.5 was estimated. Particle-bound PAHs were analyzed using gas chromatography-flame ionization detector (GC-FID). Average concentrations of WSOC, OC, and EC were 2.73 ± 2.17 (range of 0.63–9.12) µg/m3, 6.88 ± 4.94 (3.12–24.1) µg/m3, and 3.68 ± 1.58 (1.33–6.82) µg/m3, respectively, with estimated average SOC of 2.33 µg/m3, contributing 34% to total OC. The dominance of char-EC over soot-EC suggests that PM2.5 is influenced by biomass and coal combustion sources. The average of total PAHs was 1.74 ± 2.68 ng/m3. Source identification methods revealed natural gas and biomass burning, and urban traffic combustion as dominant sources of PAHs in Kuala Lumpur. A deterministic health risk assessment of PAHs was conducted for several age groups, including infant, toddler, children, adolescent, and adult. Carcinogenic and non-carcinogenic risk of PAH species were well below the acceptable levels recommended by the USEPA. Backward trajectory analysis revealed north-east air mass brought pollutants to the studied areas, suggesting the north-easterly monsoon as a major contributor to increased air pollution in Kuala Lumpur. Further work is needed using long-term monitoring data to understand the origin of PAHs contributing to SOA formation and to apply source-risk apportionment to better elucidate the potential risk factors posed by the various sources in urban areas in Kuala Lumpur.

Influence of particulate matter on human health in selected African provinces: mini-review

Air contamination influenced the human health and environmental well-being of the ecosystem. Particulate matter is a series of issues from major air pollutants in atmosphere. The aim of the review was to analyses the influence of particulate matter on human health and estimate the number of populations exposed to air pollution. The data analysed using the Environmental Benefits Mapping Analysis program model to selected African provinces. The review used 15% rollback data from the global burden disease and 5.8 µg/m³ the concentration of air pollutants from 1990 to 2013 years. The main findings of the study revealed that about 370 million (36.6%) population affected by air pollution. Besides, the risk factor associated with a population was 53,000 deaths per total population and 50,000 life-year losses. The economic value estimated to avoid a single case of particular matter on human health effect were estimated 14 billion dollars (US 2011). Priorities should be given to air quality management to improve the human and environmental health of ecosystems to reduce the global burden of disease of Africa regions.

Health risk assessment of trace elements of ambient PM2.5 under monsoon patterns

In order to identify the contribution to health risk derived from various emission sources, this study investigated monsoon variations in PM_2.5 mass and concentrations of the associated trace elements in a region with complex pollution sources in central Taiwan. This study applied the Chemical Mass Balance model to analyze the source contribution of PM_2.5. The source apportionment to obtain the risk contribution of different sources were conducted for different monsoon periods according to the monsoon patterns. In this way, the contributions of individual sources and chemicals to health risk under different monsoon types can be understood to support development of effective control strategies. Among the top contributors of PM_2.5 during the north-east monsoon were Secondary Aerosol 28.93% >Coal Boiler 19.82% >Crustal Dust 15.99%; in south-west monsoon were Coal Boiler 37.29% >Traffic Emission 21.19% >Secondary Aerosol 17.84%. The total risk of cancer was above the acceptable risk (3.07 × 10^-6), while the non-carcinogenic risk was within the acceptable range (0.262). The variation in the concentration and composition of PM_2.5 was related to the change of monsoon type. During the north-east monsoon, the air mass had a long transmission distance and the PM_2.5 concentration was relatively high. During the south-west monsoon, the air mass had a short transmission distance and the composition was mainly influenced by nearby emission sources, which resulted in higher risk due to chemical characteristics. To provide sound air quality management, attention should be paid to the composition of PM_2.5 in addition to its concentration.

Diurnal, Temporal and Spatial Variations of Main Air Pollutants Before and during Emergency Lockdown in the City of Novi Sad (Serbia)

Changes in air pollution in the region of the city of Novi Sad due to the COVID-19 induced state of emergency were evaluated while using data from permanently operating air quality monitoring stations belonging to the national, regional, and local networks, as well as ad hoc deployed low-cost particulate matter (PM) sensors. The low-cost sensors were collocated with reference gravimetric pumps. The starting idea for this research was to determine if and to what extent a massive change of anthropogenic activities introduced by lockdown could be observed in main air pollutants levels. An analysis of the data showed that fine and coarse particulate matter, as well as SO2 levels, did not change noticeably, compared to the pre-lockdown period. Isolated larger peaks in PM pollution were traced back to the Aralkum Desert episode. The reduced movement of vehicles and reduced industrial and construction activities during the lockdown in Novi Sad led to a reduction and a more uniform profile of the PM2.5 levels during the period between morning and afternoon air pollution peak, approximately during typical working hours. Daily profiles of NO2, NO, and NOX during the state of emergency proved lower levels during most hours of the day, due to restrictions on vehicular movement. CO during the state of the emergency mainly exhibited a lower level during night. Pollutants having transportation-dominated source profiles exhibited a decrease in level, while pollutants with domestic heating source profiles mostly exhibited a constant level. Considering local sources in Novi Sad, slight to moderate air quality improvement was observed after the lockdown as compared with days before. Furthermore, PM low-cost sensors’ usefulness in air quality assessment was confirmed, as they increase spatial resolution, but it is necessary to calibrate them at the deployment location.

The impact of air pollution on R&D input and output in China

This study examines the impact of air pollution on research and development (R&D) in China on the basis of province-level data for the period 2007-2016. The study discovers a significant adverse impact of air pollution on both R&D input and output in China. The estimation results in this study are robust to the different indicators used to measure R&D input and output and control for possible endogeneity problems via the instrumental variable approach. According to our estimates, if the concentration of particulate matter with a diameter of 2.5 μm or less (PM_2.5) increases by 1%, then the scales of annual R&D personnel, expenditures, and new patents will decline by 0.359%, 0.169%, and 0.293%, respectively. The findings indicate that air pollution is a considerable threat to innovation and technological progress in China.