Major air pollutants seasonal variation analysis and long-range transport of PM10 in an urban environment with specific climate condition in Transylvania (Romania)

Investigation of the spatial variation of ambient air VOCs and their effects on the health risk caused by long-term exposure in urban area

Long-term exposure to Volatile Organic Compounds (VOCs) is a health risk for citizens. In this study, the cumulative health risk of exposure to VOCs in Tehran was assessed by investigating the concentration of these pollutants in ambient air in a five-year period. Health risk assessment was calculated by the quantitative method and the carcinogenic risk level was determined using the lifetime carcinogenic risk (LCR) method. The average concentration of benzene, toluene, ortho-xylene, and ethylbenzene was 1.4-1.8, 4.8-5.4, 5-6.5, and 3.6-4 ppb, respectively. Although HQ was not greater than 1, but it was very close in the case of benzene, ortho-xylene, meta-xylene, and para-xylene. Benzene and ethylbenzene had the largest effect in the assessed health risk. So the long-term exposure of Tehran citizens to VOCs has serious health consequences for them, which could be different according to the exposure time and spatial variations.

Association between short- and medium-term exposure to air pollutants and depressive episode using comprehensive air quality index among the population in South Korea

BACKGROUND

Currently, air pollution is suggested as a risk factor for depressive episodes. Our study aimed to consider multiple air pollutants simultaneously, and continuously evaluate air pollutants using comprehensive air quality index (CAI) with depressive episode risk.

METHODS

Using a nationally representative sample survey from South Korea between 2014 and 2020, 20,796 participants who underwent health examination and Patient Depression Questionnaire-9 were included in the study. Six air pollutants (PM10, PM2.5, O3, CO, SO2, NO2) were measured for the analysis. Every air pollutant was standardized by air quality index (AQI) and CAI was calculated for universal representation. Using logistic regression, short- and medium-term exposure by AQI and CAI with the risk of depressive episode was calculated by odds ratio and 95 % confidence interval (CI). Furthermore, consecutive measurements of CAI over 1-month time intervals were evaluated with the risk of depressive episodes. Every analysis was conducted seasonally.

RESULTS

There were 950 depressive episodes occurred during the survey. An increase in AQI for short-term exposure (0-30 days) showed higher risk of depressive episode in CO, while medium-term exposure (0-120 days) showed higher risk of depressive episode in CO, SO2, PM2.5, and PM10. During the cold season, the exposure to at least one abnormal CAI within 1-month intervals over 120 days was associated with a 68 % (95 % CI 1.11-2.54) increase in the risk of depressive episode.

CONCLUSIONS

Short- and medium-term exposure of air pollution may be associated with an increased risk of depressive episodes, especially for cold season.

Comprehensive analysis of air pollution and the influence of meteorological factors: a case study of adiyaman province

Adıyaman, a city recently affected by an earthquake, is facing significant air pollution challenges due to both anthropogenic activities and natural events. The sources of air pollution have been investigated using meteorological variables. Elevated southerly winds, especially prominent in spring and autumn, significantly contribute to dust transport, leading to a decline in local air quality as detected by the HYSPLIT model. Furthermore, using Suomi-NPP Thermal Anomaly satellite product, it is detected and analyzed for crop burning activities. Agricultural practices, including stubble burning, contribute to the exacerbation of PM10 pollution during the summer months, particularly when coupled with winds from all directions except the north. In fall and winter months, heating is identified as the primary cause of pollution. The city center located north of the station is the dominant source of pollution throughout all seasons. The study established the connection between air pollutants and meteorological variables. Furthermore, the Spearman correlation coefficients reveal associations between PM10 and SO2, indicating moderate positive correlations under pressure conditions (r = 0.35, 0.52). Conversely, a negative correlation is observed with windspeed (r = -0.35, -0.50), and temperature also exhibits a negative correlation (r = -0.39, -0.54). During atmospheric conditions with high pressure, PM10 and SO2 concentrations are respectively 41.2% and 117.2% higher. Furthermore, pollutant concentration levels are 29.2% and 53.3% higher on days with low winds. Last, practical strategies for mitigating air pollution have been thoroughly discussed and proposed. It is imperative that decision-makers engaged in city planning and renovation give careful consideration to the profound impact of air pollution on both public health and the environment, particularly in the aftermath of a recent major earthquake.

Deciphering the seasonal dynamics of microplastic morphotypes and associated co-contaminants along the northwest coast of India

In the present study, the northwest coast of India, bordering the Arabian Sea, was selected to evaluate the microplastic (MP) abundance. This is the first study to emphasize the effects of different seasons on MP distribution. The collected MPs were dried, segregated, and evaluated based on their morphotype, size, color, and polymer type. A total of 1756.6, 7326.6, and 202 particles/kg of sand were estimated in the pre-monsoon, monsoon and post-monsoon seasons, respectively, with a dominance of polypropylene (PP) type of plastic in the pre-monsoon and high-density polyethylene (HDPE) in monsoon and post-monsoon seasons. HDPE and PP collected MPs during the monsoon season were further characterized for associated contaminants. Metal absorbance was detected using SEM-EDX mapping and ICP-MS. The presence of organic compounds (OCs) was analyzed using GC-MS. MPs exhibit distinct associations with metals, among which the HDPE pellet morphotype exhibits a higher range of metal adsorption. Total 61 different OCs were associated with MPs. The HDPE pellets contained the highest amounts of hydrophobic organic compounds. PP pellets were found to contain triglycerides, fatty aldehydes, and alkaloids, along with HOCs. Among morphotypes, pellet forms of MPs were found to adsorb more contaminants. These co-contaminants infiltrate the study area through sewage runoff and shoreline debris deposition, subsequently interacting with MPs. Furthermore, the MP diversity was studied by employing the MP diversity integrated index, which suggests that most of the MP diversity was observed in the pre-monsoon period. The pollution load index employed an MP risk assessment, which presented a low degree of MP contamination. In contrast, the polymer hazard index was calculated as 21650.3 in post-monsoon, placing the area under the extreme danger category. It is evident from the data that the types of MP is more important than their number. Thus, MP morphotypes have importance in the adsorption of co-contaminants.

Incidence of central retinal artery occlusion peaks in winter season

Introduction

Stroke incidence exhibits seasonal trends, with the highest occurrences observed during winter. This study investigates the incidence of central retinal artery occlusion (CRAO), a stroke equivalent of the retina, and explores its monthly and seasonal variations, as well as potential associations with weather and ambient air pollutants.

Methods

A retrospective search of medical records spanning 15 years (January 2008-December 2022) was conducted at the University Eye Hospital Tübingen, Germany, focusing on diagnosed cases of CRAO. Incidences were evaluated on a monthly and seasonal basis (winter, spring, summer, fall). Weather data (temperature, precipitation, atmospheric pressure) and concentrations of ambient air pollutants [fine particulate matter (PM2.5), coarse particulate matter (PM10), nitrogen dioxide (NO2), and ozone (O3)], were analyzed for a potential association with CRAO incidence.

Results

Out of 432 patients diagnosed with CRAO between 2008 and 2022, significantly varying incidences were observed monthly (p = 0.025) and seasonally (p = 0.008). The highest rates were recorded in February and winter, with the lowest rates in June and summer. Concentrations of NO2, PM2.5 and lower ambient air temperature (average, minimum, maximum) showed significant correlations with CRAO incidence.

Discussion

This comprehensive 15-year analysis reveals a pronounced winter peak in CRAO incidence, with the lowest occurrences in summer. Potential associations between CRAO incidence and ambient air pollutants and temperature underscore the importance of considering seasonal trends and call for further investigations to elucidate contributing factors, potentially leading to targeted preventive strategies and public health interventions.

Epigenetic mechanisms of particulate matter exposure: air pollution and hazards on human health

Environmental pollution nowadays has not only a direct correlation with human health changes but a direct social impact. Epidemiological studies have evidenced the increased damage to human health on a daily basis because of damage to the ecological niche. Rapid urban growth and industrialized societies importantly compromise air quality, which can be assessed by a notable accumulation of air pollutants in both the gas and the particle phases. Of them, particulate matter (PM) represents a highly complex mixture of organic and inorganic compounds of the most variable size, composition, and origin. PM being one of the most complex environmental pollutants, its accumulation also varies in a temporal and spatial manner, which challenges current analytical techniques used to investigate PM interactions. Nevertheless, the characterization of the chemical composition of PM is a reliable indicator of the composition of the atmosphere, the quality of breathed air in urbanized societies, industrial zones and consequently gives support for pertinent measures to avoid serious health damage. Epigenomic damage is one of the most promising biological mechanisms of air pollution-derived carcinogenesis. Therefore, this review aims to highlight the implication of PM exposure in diverse molecular mechanisms driving human diseases by altered epigenetic regulation. The presented findings in the context of pan-organic cancer, fibrosis, neurodegeneration and metabolic diseases may provide valuable insights into the toxicity effects of PM components at the epigenomic level and may serve as biomarkers of early detection for novel targeted therapies.

Impact of air pollutants on climate change and prediction of air quality index using machine learning models

The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models such as XGBoost, Random Forest, BaggingRegressor, and LGBMRegressor for the prediction of the AQI using the historical data available from 2017 to 2022. According to historical data, the AQI is highest in January, with a mean value of 104.6 g/gm, and the lowest in August, with a mean AQI value of 63.87 g/gm. Particulate matter, gaseous pollutants, and meteorological parameters were used to predict AQI, and the heat map generated showed that of all the parameters, PM2.5 has the greatest impact on AQI, with a value of 0.91. The log transformation method is used to normalize datasets and determine skewness and kurtosis. The XGBoost model demonstrated strong performance, achieving an R2 (correlation coefficient) of 0.9935, a mean absolute error (MAE) of 0.02, a mean square error (MSE) of 0.001, and a root mean square error (RMSE) of 0.04. In comparison, the LightGBM model's prediction was less effective, as it attained an R2 of 0.9748. According to the study, the AQI in Chennai has been increasing over the last two years, and if the same conditions persist, the city's air pollution will worsen in the future. Furthermore, accurate future air quality level predictions can be made using historical data and advanced machine learning algorithms.

The Joint Clean Air Actions and air quality spillovers in China

Facing severe air pollution in its North Plain, the central government of China initiated the Joint Clean Air Action (JCAA) in 2017 to facilitate pollution mitigation efforts across the region. While quite a few studies investigated the effectiveness of this regulation, much less attention is paid to the pollution spillover effects. We empirically examine the effects, and show that 1) air quality in the east of the target cities has been improved due to positive spillover of improved air quality under the JCAA; 2) the beneficiary spillover lasts for two seasons and disappeared in autumn and winter; 3) air quality in the north, south and west directions are almost not changed; 4) wind direction and topography, two determinants of atmospheric transport, have a considerable influence over the spillover effects. Our study provides a fresh perspective to understand the impacts of the JCAA policy and underlines the necessity of taking both pollution and air quality spillover effects into the cost-benefit analysis.Implications: Pollution regulations in one place may increase pollution in other places, as production and emissions are re-allocated under the incentives induced by regional-specific regulations. This phenomenon has long been recognized in the literature as pollution spillover. However, if the relevant production and emissions are not re-allocated, at least not re-allocated in large quantities, local air quality improvement induced by regulations may also benefit the neighboring areas. We call this effect air quality spillover. Both spillover effects should be rigorously evaluated, which is of scientific interest by itself and also contributes to a comprehensive cost-benefit analysis of environmental regulations.

Examination of air pollutants and their risk for human health in urban and suburban environments for two Romanian cities: Brasov and Iasi

To detect the spatial differences of atmospheric pollutants in urban and suburban areas is important for observing their aspects on regional air quality, climate, and human health. This study is focused on the evolution of PM2.5, PM10, NOx and SO2, concentrations, and meteorological parameters from 2010 to 2022, at urban and suburban area in the two Romanian city: Brasov and Iasi. The daily patterns of most pollutants in urban and suburban areas, are strongly linked to land-traffic emissions. The seasonal differences were observation of the studied air pollutants displays visible decreasing in warm period and increased concentrations in cold period. Significant higher (25%- Brasov, 28%- Iasi) PM10 were found in urban area concentration probably caused by enhanced vehicular emissions over these areas induced by urban planning and mobility policies. The average relative risk caused by PM10 for all-cause mortality in the urban region was 1.021 (±0.004) in Brasov, and significantly higher in Iasi 1.030 (±0.005). In suburban regions this risk was lower with 33 % 1.014 (±0.006) in Brasov and 30 % 1.021 (±0.003) in Iasi. The main objective of this research was to identify the difference of air pollutants and meteorological parameters in the urban and suburban region of the studied city.

Analysis of DNA methylation at birth and in childhood reveals changes associated with season of birth and latitude

BACKGROUND

Seasonal variations in environmental exposures at birth or during gestation are associated with numerous adult traits and health outcomes later in life. Whether DNA methylation (DNAm) plays a role in the molecular mechanisms underlying the associations between birth season and lifelong phenotypes remains unclear.

METHODS

We carried out epigenome-wide meta-analyses within the Pregnancy And Childhood Epigenetic Consortium to identify associations of DNAm with birth season, both at differentially methylated probes (DMPs) and regions (DMRs). Associations were examined at two time points: at birth (21 cohorts, N = 9358) and in children aged 1-11 years (12 cohorts, N = 3610). We conducted meta-analyses to assess the impact of latitude on birth season-specific associations at both time points.

RESULTS

We identified associations between birth season and DNAm (False Discovery Rate-adjusted p values < 0.05) at two CpGs at birth (winter-born) and four in the childhood (summer-born) analyses when compared to children born in autumn. Furthermore, we identified twenty-six differentially methylated regions (DMR) at birth (winter-born: 8, spring-born: 15, summer-born: 3) and thirty-two in childhood (winter-born: 12, spring and summer: 10 each) meta-analyses with few overlapping DMRs between the birth seasons or the two time points. The DMRs were associated with genes of known functions in tumorigenesis, psychiatric/neurological disorders, inflammation, or immunity, amongst others. Latitude-stratified meta-analyses [higher (≥ 50°N), lower (< 50°N, northern hemisphere only)] revealed differences in associations between birth season and DNAm by birth latitude. DMR analysis implicated genes with previously reported links to schizophrenia (LAX1), skin disorders (PSORS1C, LTB4R), and airway inflammation including asthma (LTB4R), present only at birth in the higher latitudes (≥ 50°N).

CONCLUSIONS

In this large epigenome-wide meta-analysis study, we provide evidence for (i) associations between DNAm and season of birth that are unique for the seasons of the year (temporal effect) and (ii) latitude-dependent variations in the seasonal associations (spatial effect). DNAm could play a role in the molecular mechanisms underlying the effect of birth season on adult health outcomes.

Impact of COVID-19 restrictions on hourly levels of PM10, PM2.5 and black carbon at an industrial suburban site in northern Spain

Due to the COVID-19 pandemic, lockdown restrictions were established around the world. Many studies have assessed whether these restrictions affected atmospheric pollution. Comparison between them is difficult as the periods of time considered are generally not the same and thus, different conclusions may be reached. Besides, most of them consider mean daily pollutant concentration, despite differences being observed according to the time of day. In this study, the hourly levels of PM10, PM2.5 and black carbon (BC) in an industrial suburban area in the north of Spain were analysed from May 2019 to June 2020 and compared with those from the literature, using the same period in each case. In general, the highest concentrations were reached when the wind direction came from the southwest (where a steelworks, a coal-fired power plant and other industries are located) and during the night-time, both before and during the lockdown. The highest concentrations of PM10, PM2.5 and BC were observed from December to February (on average: 45, 17 and 1.3 μg m^-3, respectively). The decrease/increase in those pollutants levels during the lockdown were found to be highly dependent on the period considered. Indeed, PM10 can be found to decrease by up to 39% or increase by 12%; PM2.5 can decrease by 21% or increase by up to 36%; and BC, although it generally decreases (by up to 42%), can increase by 7.4%.

Spatiotemporal analysis and forecasting of air quality in the greater Dhaka region and assessment of a novel particulate matter filtration unit

Bangladesh is one of the most polluted nations in the world, with an average Air Quality Index (AQI) of 161 in 2021; its capital, Dhaka, has the worst air quality of any major city in the world. The present study aims to analyze the spatiotemporal distribution of air quality indicators in the greater Dhaka region, forecast weekly AQI, and assess the performance of a novel particulate matter filtration unit in removing particulate matter. Air quality indicators remained highest during the dry season with an average of 128.5 μm/m^3, while the lowest concentration was found in the monsoon season with an average of 19.096 μm/m³. Analysis revealed a statistically significant annual increasing trend of CO, which was associated with the growing number of brick kilns and usage of high-sulfur diesel. Except for the pre-monsoon AQI, concentrations of both seasonal and yearly AQI and PM_2.5 showed decreasing trend, though predominantly insignificant, demonstrating the improvement in air quality. Prevailing winds influenced the seasonal distribution of tropospheric CO & NO₂. The study also employed a seasonal autoregressive integrated moving average (ARIMA) model to forecast weekly AQI values. ARIMA (3,0,4) (3,1,3) at the 7-periodicity level performed best forecasting the AQI values among all developed models with low root mean square error (RMSE)-29.42 and mean absolute percentage error (MAPE)-13.11 values. The predicted AQI values suggested that the air quality would remain unhealthy for most weeks. The experimental simulation of the particulate matter filtration unit, designed in the shape of a road divider, generated substantial cyclonic motion while maintaining a very minimal pressure drop. In the real-world scenario, using only cyclonic separation and dry deposition, the suggested air filtration system removed 40%, 44%, and 42% of PM_2.5, PM₁₀, and TSP, respectively. Without employing filters, the device removed significant amounts of particulate matter, implying enormous potential to be used in the study area. The study could be useful for policy makers to improve urban air quality and public health in Bangladesh and in other developing countries.

Temporal evolution of PM2.5, PM10, and total suspended particles (TSP) in the Ciuc basin (Transylvania) with specific microclimate condition from 2010 to 2019

Modern societies are characterized by increased air pollution, and particulate matter (PM) is one of the most significant air pollutants and is a major environmental health problem. Therefore, long- and short-term exposure via inhalation, ingestion, and dermal absorption of particulate matter may cause series health issues, such as cardio pulmonary and lung cancer disease. Air pollutants accumulation is significantly higher in closed regions or basins characterized by strong thermal inversions, especially during the cold period such in case of the Ciuc basin. The aim of this study was to carry out the time series analysis of PM_2.5, PM₁₀ and TSP in the Ciuc basin for the period 2010-2019, in order to decipher the main characteristics of air pollution in this region.The data obtained were analyzed on a daily, monthly and annual basis by different statistical methods. The average monthly concentration of TSP (60.03 µg m^-3), PM₁₀ (19.21 µg m^-3) and PM_2.5 (14.73 µg m^-3) particulate matter in the studied regions varied between 29.84-134.79 µg m^-3, 4.38-63.51 µg m^-3 and 4.01-54.41 µg m^-3, respectively. Regarding the ratio of PM_2.5 and PM₁₀ in the total particulate matter (TPM) was 0.25 and 0.33. Due to meteorological factors and emission fluctuations, particulate matter exhibits high seasonal variations, therefore the highest concentrations were recorded during the cold period, while the lowest values were observed in summer. The percentage of PM₁₀ exceedances (50 µg m^-3) represents 24.8% in winter, meanwhile in autumn and spring a significantly lower exceedances percentage was observed, 2.6% and 1.7%, respectively. The correlation analysis revealed that the correlation level of the studied pollutants varied between 0.73-0.78.

VOC species controlling O3 formation in ambient air and their sources in Kaifeng, China

The concentration of ozone has been in a rising crescendo in the last decade while the fine particles (PM_2.5) is gradually decreasing but still at a high level in central China. Volatile organic compounds (VOCs) are the vital precursors of ozone and PM_2.5. A total of 101 VOC species were measured in four seasons at five sites from 2019 to 2021 in Kaifeng. VOC sources and geographic origin of sources were identified by the positive matrix factorization (PMF) model and the hybrid single-particle Lagrangian integrated trajectory transport model. The source-specific OH loss rates (L_OH) and ozone formation potential (OFP) were calculated to estimate the effects of each VOC source. The average mixing ratios of total VOCs (TVOC) were 43.15 parts per billion (ppb), of which the alkanes, alkenes, aromatics, halocarbons, and oxygenated VOCs respectively accounted for 49%, 12%, 11%, 14%, and 14%. Although the mixing ratios of alkenes were comparatively low, they played a dominant role in the L_OH and OFP, especially ethene (0.55 s^-1, 7%; 27.11 μg/m³, 10%) and 1,3-butadiene (0.74 s^-1, 10%; 12.52 μg/m³, 5%). The vehicle-related source which emitted considerable alkenes ranked as the foremost contributing factor (21%). Biomass burning was probably influenced by other cities in the western and southern Henan and other provinces, Shandong and Hebei.

A biochemical and morphological study with multiple linear regression modeling-based impact prediction of ambient air pollutants on some native tree species of Haldwani City of Kumaun Himalaya, Uttarakhand, India

The current study was conducted around the province of Haldwani City, Uttarakhand, India, to understand the seasonal variation of ambient air pollutants (PM_2.5, PM₁₀, SO₂, and NO₂) and their impact on four tree species, i.e., neem (Azadirachta indica), mountain cedar (Toona ciliate), bottlebrush (Callistemon citrinus), and guava (Psidium guajava) during 2020-2021. Multiple linear regression (MLR)-based prediction analysis showed that the selected air quality variables (PM_2.5, PM₁₀, SO₂, and NO₂) had a significant impact on the biochemical responses of selected tree spp. including, pH, ascorbic acid (AA), total chlorophyll content (T. Chl.), relative water content (RWC), and dust deposition potential. In this, the coefficient of variance (R²) of the developed models was in the range of 0.70-0.98. The ambient air pollutants showed significant seasonal variations as depicted by using the air pollution tolerance index (APTI) and anticipated performance index (API). The tree species from polluted sites observed more pollution tolerance than the tree species from the control site. Regression analysis showed a significant positive association between the biochemical characteristics and APTI, with the highest influence by AA (R² = 0.961) followed by T. Chl., RWC, and pH. The APTI and API score was observed as maximum for A. indica and minimum for C. citrinus. The impact of air pollutants on the morphology of foliar surface was investigated by the scanning electron microscopy (SEM) and recorded various dust deposition patterns, stomatal blockages, and damage of guard cells in the trees growing along the polluted site (S2). The present study can assist environmental managers to examine the pollution-induced variables and develop an effective green belt for combating air pollution in polluted areas.

Characterization of Polycyclic Aromatic Hydrocarbons (PAHs) associated with fine aerosols in ambient atmosphere of high-altitude urban environment in Sikkim Himalaya

Polycyclic Aromatic Hydrocarbons (PAHs) compounds are ubiquitous in ambient air due to their persistence, carcinogenicity, and mutagenicity. Gangtok being one of the cleanest cities in India located in Eastern Himalayan region, witnesses high developmental activities with enhanced urbanization affecting the ambient air quality. The present study aims to measure PM_2.5 and PAHs in the ambient atmosphere of the Sikkim Himalaya to understand the influence of natural and anthropogenic activities on aerosol loading and their chemical characteristics. The PM_2.5 samples were collected and analysed for the duration from Jan 2020 to Feb 2021.The seasonal mean concentrations of PM_2.5 and PAHs were observed to be high during autumn and low during summer season. Overall, the annual mean concentration of PM_2.5 was found higher than the prescribed limit of World Health Organization and National Ambient Air Quality Standards. The concentration of the 16 individual PAHs were found to be highest during autumn season (55.26 ± 37.15 ng/m³). Among the different PAHs, the annual mean concentration of fluorene (3.29 ± 4.07 ng/m³) and naphthalene (1.15 ± 3.76 ng/m³) were found to be the highest and lowest, respectively. The Molecular Diagnostic Ratio (MDR) test reveals higher contribution from heavy traffic activities throughout the winter and autumn seasons. The other possible sources identified over the region are fossil fuel combustion, and biomass burning. The multivariate statistical analysis (Multifactor Principal Component Analysis) also indicates a strong association between PM_2.5 /PAHs and meteorological variables across the region in different seasons. The precipitation and wind pattern during the study period suggests that major contribution of the PM_2.5 and PAHs were from local sources, with minimal contribution from long-range transport. The findings are important for comprehending the trends of PAH accumulation over a high-altitude urban area, and for developing sustainable air quality control methods in the Himalayan region.

The relationship between PM10 and meteorological variables in the mega city Istanbul

PM₁₀, one of the air pollutants, occurs regularly in İstanbul during the winter months, namely in December, January, and February. PM₁₀ pollutant is affected by numerous factors. Among these factors are various meteorological variables and climatological factors. This article aims to determine the relationship between PM₁₀ and meteorological variables (wind speed, wind direction, temperature, and relative humidity) and to interpret these results. PM₁₀ and meteorological data were examined between 2011 and 2018. To determine the relationship, multiple linear regression, Pearson's correlation coefficient (PCC), Spearman's rank correlation, Kendall Tau correlation, autocorrelation function (ACF), cross-correlation function (CCF), and visuals were determined using the R program (open-air) packages. In the study, the relationship between wind, temperature, and relative humidity with PM₁₀ was determined, and it was observed that the PM₁₀ concentration was maximum between January and February. PM₁₀ concentrations have a positive relationship with relative humidity and wind direction, while a negative relationship with wind speed and temperature was observed. The correlation values for relative humidity and temperature were found to be 0.01 and - 0.15, respectively. Furthermore, the relationship between wind speed and PM₁₀ was calculated from multiple linear regression model, and the estimated value was - 0.12 while looking at the wind direction value, it was approximately 0.03.

Assessment of meteorological settings on air quality modeling system-a proposal for UN-SDG and regulatory studies in non-homogeneous regions in Brazil

Air quality models are essential tools to meet the United Nations Sustainable Development Goals (UN-SDG) because they are effective in guiding public policies for the management of air pollutant emissions and their impacts on the environment and human health. Despite its importance, Brazil still lacks a guide for choosing and setting air quality models for regulatory purposes. Based on this, the current research aims to assess the combined WRF/CALMET/CALPUFF models for representing SO₂ dispersion over non-homogeneous regions as a regulatory model for policies in Brazilian Metropolitan Regions to satisfy the UN-SDG. The combined system was applied to the Rio de Janeiro Metropolitan Area (RJMA), which is known for its physiographic complexity. In the first step, the WRF model was evaluated against surface-observed data. The local circulation was underestimated, while the prevailing observational winds were well represented. In the second step, it was verified that all CALMET three meteorological configurations performed better for the most frequent wind speed classes so that the largest SO₂ concentrations errors occurred during light winds. Among the meteorological settings in WRF/CALMET/CALPUFF, the joined use of observed and modeled meteorological data yielded the best results for the dispersion of pollutants. This result emphasizes the relevance of meteorological data composition in complex regions with unsatisfactory monitoring given the inherent limitations of prognostic models and the excessive extrapolation of observed data that can generate distortions of reality. This research concludes with the proposal of the WRF/CALMET/CALPUFF air quality regulatory system as a supporting tool for policies in the Brazilian Metropolitan Regions in the framework of the UN-SDG, particularly in non-homogeneous regions where steady-state Gaussian models are not applicable.

Ozone pollution in London and Edinburgh: spatiotemporal characteristics, trends, transport and the impact of COVID-19 control measures

Air pollution remains the most serious environmental health issue in the United Kingdom while also carrying non-trivial economic costs. The COVID-19 lockdown periods reduced anthropogenic emissions and offered unique conditions for air pollution research. This study sources fine-granularity geo-spatial air quality and meteorological data for the capital cities of two UK countries (i.e. England's capital London and Scotland's capital Edinburgh) from the UK Automatic Urban and Rural Network (AURN) spanning 2016–2022 to assess long-term trends in several criteria pollutants (PM10, PM2.5, SO2, NO2, O3, and CO) and the changes in ozone pollution during the pandemic period. Unlike other studies conducted thus far, this research integrates several tools in trend estimation, including the Mann-Kendall test, the Theil-Sen estimator with bootstrap resampling, and the generalized additive model (GAM). Moreover, several investigations, including cluster trajectory analysis, pollution rose plots, and potential source contribution function (PSCF), are also employed to identify potential origin sources for air masses carrying precursors and estimate their contributions to ozone concentrations at receptor sites and downwind areas. The main findings reveal that most of the criteria pollutants show a decreasing trend in both geographies over the seven-year period, except for O3, which presents a significant ascending trend in London and a milder ascending trend in Edinburgh. However, O3 concentrations have significantly decreased during the year 2020 in both urban areas, despite registering sharp increases during the first lockdown period. In turn, these findings indicate on one hand that the O3 generation process is in the VOC-limited regime in both UK urban areas and, on the other hand, confirm previous findings that, when stretching the analysis period, diminishing ozone levels can lead to NOx reduction even in VOC-controlled geographies. Trajectory analysis reveals that northern Europe, particularly Norway and Sweden, is a principal ozone pollution source for Edinburgh, whereas, for London, mainland Europe (i.e., the Benelux countries) is another significant source. The results have important policy implications, revealing that effective and efficient NOx abatement measures spur ozone pollution in the short-term, but the increase can be transient. Moreover, policymakers in London and Edinburgh should consider that both local and transboundary sources contribute to local ozone pollution.

Geospatial passives for dynamic vegetation monitoring around thermal power plants

As point sources of pollution, thermal power plants (TPPs) emanate hazardous gaseous and particulate matter that are of significant detriment to surrounding biological landscapes. To provide support to ecological conservation and resource management in developing countries, this study aims to establish a cost effective and robust geospatial methodology for dynamic vegetation monitoring of local pollution zones around TPPs using passive satellite-based indicators. The extent and severity of hazardous bio-influence around four TPPs is identified and monitored for a period of 5 years, using vegetation indices (VIs). High correlations of vegetation health with distance from TPPs have also been identified, signifying the hazardous impact of TPP emissions to surrounding vegetation. Variations in behavior of zones of high pollutant concentration are observed both in space and time, as a response to local seasonal weather, nature of fuel used in TPP, and type and areal coverage of vegetation around the power plants. Winter and Monsoon seasons have been identified to create favorable conditions for sustaining high pollution concentration around TPPs, and hence, the extent of hazardous bio-influence zones in these seasons is maximum. Moreover, oil-based power plant is revealed to be associated with large radial zones of degraded vegetation around it and, therefore, poses greater ecological hazard than gas-powered TPPs. The average bio influence zone measured for the test sites has been found to be 1660 m that ranges from 1600 to 1730 m for different power plants, explaining variable behavior of the used fuel and surrounding vegetation conditions. In this way, the study stresses upon the importance of geospatial data and analytical frameworks in reliable and economical monitoring of environmental pollution associated with anthropogenic sources, using passive environmental indices derived from remote data.

Effect of association of temperature and pollutant levels on COVID-19 spread over Jaipur

The association of temperature and air pollutants is a very prominent factor which significantly affects human health and may cause diseases such as respiratory illness, cardiovascular mortality in spreading of different pathogenic diseases. The pandemic due to covid-19 infection may be affected by temperature and concentration of pollutants. Jaipur is one of the most polluted cities in Rajasthan of India as per World Health Organization, 2016; also, Jaipur city has a hot semi-arid climate with extremely hot summers. This fact tempered us to examine the impact of the association of temperature and pollutants on corona-virus infection in humans over Jaipur. Analysis was conducted by correlating air pollutants (PM10, PM2.5, NO2, SO2, CO) on seasonal variations because the temperature is one of the major factors in changing seasons. Association between the number of Covid cases and temperature in Jaipur was observed during December 2019 to December 2020. Seasonal analysis indicated that the intensity of Covid-19 infection varied according to increase or decrease in temperature.

Assessment of the exposure to PM2.5 in different Lebanese microenvironments at different temporal scales

The weak potential of using the sole outdoor concentrations to represent personal exposure to PM_2.5 is confirmed by the literature; therefore, it is important to account for a person's movements over time when estimating the short-term personal air pollution exposure within different microenvironments (MEs). This study is an example of applying an assessment method of the exposure to PM_2.5 in different microenvironments at different temporal scales. A low-cost particle counter (the Dylos 1700) was used; its performance was validated in comparison with equivalent instruments such the SidePak AM520 Personal Aerosol Monitor (R² = 0.89). This validation also provided a function to convert measured particle number concentrations (PNCs) into calculated particle mass concentrations. The 150 profiles that was collected on a minute-by-minute basis regarding PM_2.5 concentration from December 2018 to May 2021 highlight the influence of individual activities and contextual factors on the air quality, so that Lebanon's annual PM_2.5 mean (24.2 µg⁄m³) is 142% higher than the World Health Organization (WHO) annual mean guideline (10 µg⁄m³). Winter is the most polluted period due to the increased application of space heating devices. Additionally, the occurrence of dusty winds during the spring period leads to the elevated levels of dispersed PM_2.5. Simultaneously, the rural zones are more polluted than urban ones due to the usage of more traditional heating equipment, in addition to the usage of chemical products like pesticides and fertilizers in agricultural activities in such areas. Furthermore, the (outdoor-indoor-transport) MEs indicate that the transport and indoor MEs have similar levels of suspended fine particulates, while outdoor MEs are less polluted. Studies based on the personal exposure to PM_2.5 were generally applied on specific and limited places such as schools, workplaces, or residences. The study aims to shed light on the modern method in an attempt to estimate the personal exposure to PM_2.5 and to inspire similar studies to achieve the maximum efficiency.

Comparison of metropolitan cities for mortality rates attributed to ambient air pollution using the AirQ model

In the present study, the air pollution dynamics of the metropolitan cities of Balıkesir, Bursa, Istanbul, Kocaeli, Sakarya and Tekirdağ in the Marmara Region, which is the geographical region with the highest urban and industrial activity in Turkey, were examined for the time period between 2016 and 2019. Annual changes in the cities in terms of air pollution, which was examined with a focus on the PM_2.5 parameter as indicated by United Nations (UN) Sustainable Development Goals (SDGs); differences in the cities by years; and the seasonal changes in air pollution in the cities were investigated. Additionally, mortality rates attributed to air pollution were calculated with the AirQ + software based on integrated exposure-response function recommended by the World Health Organization (WHO) and the UN using city-scale statistics of fatal disease cases that can be attributed to air pollution. It was determined that all cities in the Marmara Region study area exceeded the limit PM_2.5 values specified by the European Union (EU) in the years 2016, 2017 and 2018 while only Kocaeli and Tekirdağ were below the limit values in 2019. The limit values specified by the WHO were exceeded in all cities in each year. A total of 46,920 premature deaths attributed to the exceedance of WHO limit values were calculated for the years 2016, 2017, 2018 and 2019 with 11,895, 13,853, 11,748 and 9,429, respectively. Determining national limit values for the PM_2.5 parameter, which is among the most important factors of air pollution, and monitoring it in a sustainable manner using a sufficient number of well-equipped stations is of great importance. This way, national, regional and urban action plans regarding the impact of air pollution on human health, as indicated by UN SDGs, can be prepared.

Impact of COVID‐19 lockdown on the elemental profile of PM 10 present in the ambient aerosol of an educational institute in Kolkata, India

Reduction in air pollution level was prime observation during COVID‐19 lockdown globally. Here, the study was conducted to assess the impact of lockdown on the elemental profile of PM₁₀ in ambient aerosol to quantify the elemental variation. To quantify the variation, phase‐wise sampling of air pollutants was carried out using the gravimetric method for PM₁₀, while NO₂ and SO₂ were estimated through the chemiluminescence and fluorescent spectrometric method respectively. The elemental constituents of PM₁₀ were carried out using an Inductively Coupled Plasma Optical Emission Spectrometer and their source apportionment was carried out using the Positive Matrix Factorization model. The results showed that PM₁₀, NO₂ and SO₂ reduced by 86.97%, 83.38%, and 88.60% respectively during the lockdown sampling phase. The highest mean elemental concentration reduction was found in Mn (97.47%) during the lockdown. The inter‐correlation among the pollutants exhibited a significant association indicating that they originate from the same source. The metals like Mn and Cu were found at a higher concentration during the lockdown phase corresponding to vehicular emissions. The comparative analysis of the elemental profile of PM₁₀ concluded that the lockdown effectuated in reduction of the majority of elements present in an aerosol enveloping metropolitan like Kolkata.

Visibility Prediction over South Korea Based on Random Forest

In this study, the visibility of South Korea was predicted (VISRF) using a random forest (RF) model based on ground observation data from the Automated Synoptic Observing System (ASOS) and air pollutant data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Atmosphere Monitoring Service (CAMS) model. Visibility was predicted and evaluated using a training set for the period 2017–2018 and a test set for 2019. VISRF results were compared and analyzed using visibility data from the ASOS (VISASOS) and the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) (VISLDAPS) operated by the Korea Meteorological Administration (KMA). Bias, root mean square error (RMSE), and correlation coefficients (R) for the VISASOS and VISLDAPS datasets were 3.67 km, 6.12 km, and 0.36, respectively, compared to 0.14 km, 2.84 km, and 0.81, respectively, for the VISASOS and VISRF datasets. Based on these comparisons, the applied RF model offers significantly better predictive performance and more accurate visibility data (VISRF) than the currently available VISLDAPS outputs. This modeling approach can be implemented by authorities to accurately estimate visibility and thereby reduce accidents, risks to public health, and economic losses, as well as inform on urban development policies and environmental regulations.

Dilution and dispersion of particulate matter from abandoned mine sites to nearby communities in Namibia

In this study, the HYSPLIT model was used to model the dilution and dispersion of particulate matter in the surrounding of two abandoned mine sites (Klein Aub and Oamites) to the nearby communities. The simulations indicated a relationship between mass concentrations and meteorological parameters as well as their impacts on the dilution and dispersion of particulate matter. Dispersion simulations of the non-residential point indicated that the community of Klein Aub is mainly affected by tailings dust through the Easterly wind which transports tailings contaminated dust to the residential area during winter. Oamites dispersion simulations of the residential point located at 23°47′24.6 S; 016°38′46.2 E indicated that this point is not directly affected by the tailings dust. The results suggest that the influence of the wind direction and wind speed is more on the dispersion than dilution. The wind or lack of thereof can cause the particulate matter to move or accumulate around the pollution source. The dilution of particulate matter was mainly influence by the temperature, with generally low dilution observed in the cold winter months.

A Comparative Study on Air Pollution Characteristics in Four Key Cities during 2013 in Guangxi Province, China

Based on ambient air quality data of the four key cities (Nanning, Liuzhou, Guilin, and Beihai) in Guangxi, China, along with an analysis of the main emission sources, topographic features, weather conditions, and backward trajectories, the variation of main air pollutants and pollution episodes in the four cities were studied. Results showed that air pollution was most serious in Liuzhou and Guilin, followed by Nanning and Beihai. PM2.5 was the dominant pollutant in each city, followed by O3, PM10, and NO2. Concentrations of SO2 and CO did not exceed their National Ambient Air Quality Standard Grade II limit values. In the cities, the concentrations of PM2.5, PM10, NO2, and SO2 were high during fall and winter and low during spring and summer, while the concentrations of O3 were high during fall and low during the other seasons. Concentrations of CO were low during summer and high during the other seasons in Nanning and Liuzhou, while they were high during spring and winter and low during summer and fall in Guilin and Beihai. In these cities, pollution episodes resulted mainly from stagnant accumulation and showed characteristics of regional pollution. However, pollution levels and durations for each city were different due to differences in main pollution sources, local geography, and weather conditions. The influences of air masses on the four cities were similar. They were mainly influenced by local emission sources in the spring, while during autumn, long-distance transportation from Hunan and Hubei was significant. In winter, air pollution in Nanning and Beihai was mostly affected by local emission sources, and that in Liuzhou and Guilin was mainly affected by long-distance transportation from the south and northeast of Guangxi.