Exposure and health: A progress update by evaluation and scientometric analysis

An overview of reviews of breastfeeding barriers and facilitators: Analyzing global research trends and hotspots

Breastfeeding is the most cost-effective intervention for reducing infant morbidity and mortality, offering benefits for infants and mothers. Despite extensive promotion, global adherence remains below 50 %, resulting in significant clinical, economic, and environmental impacts. Thus, this overview of reviews aims to synthesize barriers and facilitators of breastfeeding, analyze research trends, and identify gaps to guide future research. A comprehensive literature search was conducted, including systematic reviews that examine these factors. The search covered seven electronic data repositories. The methodological quality was assessed using the Risk of Bias in Systematic Reviews tool. Bibliometric analysis focused on identifying top journals, authors, and countries, assessing their impact, and exploring trends over time. Findings were classified and analyzed thematically through line-by-line coding, theme description, and analytical formulation. A total of 123 reviews were included, mostly of high quality and published in top journals. Key trends comprised a growing focus on psychosocial and cultural factors, increased representation from low- and middle-income countries, and improved methodological rigor. However, geographical representation remains biased towards high-income countries, and some breastfeeding outcomes need further exploration. Thematic analysis revealed four categories: Therapeutic and care interventions; Support networks and education; Maternal-infant health issues; and Societal and environmental context. In conclusion, this overview of reviews identifies barriers and facilitators of breastfeeding and emphasizes the need for more inclusive research and tailored support. Addressing gaps in evidence for enhancing healthcare systems and policies can improve breastfeeding practices and outcomes worldwide.

Years of Life Lost Attributable to Estimated Air PM2.5 Using High-Resolution Satellite Data in a Region of Iran

In this study, we aimed to investigate the impact of predicted particulates with a diameter of 2.5 μm or less (PM2.5) using the satellite data on Years of Life Lost (YLL) as a health burden of air pollution. A 2-stage methodology was used in order to predict PM2.5 using Aerosol Optical Depth (AOD). The predicted PM was corrected for its bias through Bland-Altman method and observed data. Relative Risk (RR), Attributable Fraction (AF), and Attributable Number (AN) of YLL were estimated as the effect of PM2.5 on health. Based on the minimum value as the optimum value of PM, statistically a significant cumulative dose-response association was found. The significant association was mainly observed between lags 4 and 13. Also, based on the scenario, the total estimated YLL attributable to air pollution was 74227 years, with an AF of 0.45 which was statistically significant (95% CI: 0.14, 0.65). Based on the median value as the second scenario, there was not cumulative significant dose-response association. The subgroup analysis revealed that females and the elderly exhibited higher PM2.5-related YLL compared to males and younger, respectively. Totally, the study revealed that impact of the predicted PM on YLL was significant when we selected the minimum value as reference. While, the impact was insignificant when we changed it to median value. This result highlights the important effect of reference value selection on the interpretation of dose-response and lag-response associations between PM2.5 and YLL which should be addressed in next studies.

Illusion of Incense Smoke and Associated Health Risk: An Investigation of Ocular and Respiratory Particulate Deposition

The widespread use of incense in indoor environments, particularly in cultural and religious practices, poses significant health risks due to particulate matter (PM) emissions. This study examines the chemical composition, particle morphology, and deposition dynamics of PM from four types of incense: Cup dhoop, Cone dhoop, Natural Incense Powder, and Agarbatti. Advanced analytical techniques, including SEM, FTIR, ICP-MS, and CAM, were employed to characterize particles, focusing on their size, elemental makeup, and surface properties. Particle sizes ranged from 12.02 µm to 422.3 nm, with lenses showing higher concentrations than filters. Elements such as sodium (300 µg/m3) and mercury (1.99 µg/m3) were prominent in lenses, while arsenic (6.2 µg/m3) and cadmium (0.19 µg/m3) were dominant in filters. Neurotoxins like aluminum, lead, and mercury highlighted potential risks, including oxidative stress and systemic toxicity. Deposition modeling revealed age-related differences, with children (8 years) experiencing higher pulmonary deposition (16.8% for Cup dhoop), while adults (21 years) showed greater head region deposition (37.6% for Agarbatti). Hydrophobic particles in filters (contact angle 119.2°) contrasted with hydrophilic particles in lenses (69.8°), increasing ocular exposure risks. Cone dhoop exhibited the highest cancer risk, affecting 5 in 100,000 individuals, emphasizing its hazardous nature. FTIR identified microplastics like polypropylene and polyvinyl chloride, known to adsorb and transport heavy metals, compounding health risks. These findings highlight the critical health impacts of incense emissions, particularly for children, and underscore the urgent need for stricter regulations, improved ventilation, and public awareness to mitigate exposure.

Spatio-temporal variation of air quality and its driving factors in Jinan and Qingdao during 2014-2022

Over the past 20 years, urbanization of Shandong Province has strongly supported the rapid growth and sustained transformation of economy, however, this region has suffered from serious atmospheric pollution due to intense human activity. Identifying and qualifying the spatio-temporal variation of air pollution and its driving forces of Shandong Province would help in the formulation of effective mitigation policies. A deep understanding of the coupling relationship between air quality and socioeconomic drivers was essential for evaluating the quality of urbanization and long term sustainability. Hence, this study investigates the spatio-temporal variation and its driving factors of air quality in Jinan and Qingdao during 2014-2022. The air quality index (AQI), PM2.5, PM10, CO, SO2 and NO2 showed a seasonal pattern with higher values in winter and lower values in summer, however, O3 showed lower values in winter and higher value in summer. AQI quality for Qingdao surpassed Jinan, but AQI improvement rates of Jinan surpassed Qingdao, which means higher AQI quality in Qingdao and faster AQI improvement in Jinan. Spearman correlation analysis (SCA), gray relational analysis (GRA) and entropy weight method (EMW) were used to evaluated the interrelations between AQI and pollutant-emission / economic-development / urban-construction index. The primary driving factors were industrial smoke (dust) emissions (SCA, r = 0.94), value-added of secondary industry (GRA, r = 0.68), value-added of secondary industry (EWM, w = 0.125) and per capita public green space area (EWM, w = 0.104) for Jinan. But the primary driving factors were value-added of secondary industry (SCA, r = -0.92), value-added of primary industry (GRA, r = 0.77), value-added of primary industry (EWM, w = 0.147) and green coverage rate of urban built-up areas (EWM, w = 0.129) for Qingdao. These results could provide valueable, meaningful and significant supporting and framework for future air quality management and improvement.

Particulate toxic elements' oxidative potential and gastrointestinal bioaccessibility features in the vicinities of coal-fired mineral processing industries, India

Particulate matter (PM) poses significant health risks due to its ability to generate reactive oxygen species (ROS) and transport toxic metal(loid)s into the human body. In this study, an in vitro physiologically based extraction test (PBET) method, allowing the simulation of the gastric phase (GPh) and intestinal phase (IPh) of human digestion, was applied to evaluate bioaccessibility of eleven potentially toxic elements (Al, As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) in airborne particulate matter (APM) samples collected from an urban-residential area in Chhattisgarh, India. Additionally, oxidative potential (OP) was assessed using the dithiothreitol (DTT) assay for a comprehensive understanding of PM toxicity. The bioaccessibility of metal(loid)s varied significantly across phases, with gastric phase solubility upto ~ 75%, attributed to its lower pH enhancing metal dissolution. Elevated DTT responses were recorded for PM10 and PM2.5, driven primarily by Fe, Zn, and Pb, underlining their pivotal role in oxidative stress generation. Correlation analyses demonstrated strong associations between bioaccessible fractions and OP, especially in the GPh. The findings advance understanding by linking bioaccessibility with ROS generation and highlight the importance of particle size and solubility in assessing the health risks posed by PM. These insights provide a foundation for improved risk assessments and mitigation strategies targeting emissions from high-temperature processing industries, and vehicular activities, on a global scale.

Comprehensive assessment of potential toxic elements in surface dust of community playgrounds in Xi'an, China

To identify the key factors for managing and controlling potential toxic elements (PTEs) in surface dust of urban community playgrounds, this study comprehensively analyzed the content, pollution characteristics, eco-health risks, and sources of commonly concerned PTEs in surface dust of Xi'an community playgrounds. The average levels of Cd, Hg, Cu, Cr, Ba, Zn and Pb in the dust were 2.2, 0.27, 1.4 × 102, 2.1 × 102, 1.7 × 103, 2.9 × 102, 1.5 × 102 mg kg-1, respectively, exceeding the soil background values. The main sources of PTEs in the dust were natural source, mixed source of construction and weathering of entertainment facilities, traffic source, and industrial source, accounting for 24.9%, 45.7%, 18.1%, and 11.3%, respectively. The contamination and ecological risk of PTEs in the dust were elevated, and Cd and industrial source were identified as the primary contributors. The non-carcinogenic risks for different age groups were within a safe range, but the cancer risk was high, especially for toddlers and the elderly. It is worth noting that the cancer risk based on the minimum values of key exposure parameters for toddlers, preschool children, children, and teenagers has exceeded the acceptable level. According to the results of source-oriented health risk assessment, the traffic source was identified as the main contributors of health risk, and Ni was a particularly concerned PTE. These findings can provide the scientific basis for controlling PTEs pollution in urban community playgrounds and the guidance for protecting residents' health.

Burning of municipal waste in household furnaces and the health of their owners

The aim of the study was to determine the scale of emission and airborne dispersion of selected pollutants (PM2.5, PM10, TVOC, HCHO) associated with the combustion of various types of municipal waste (MW), its mixed stream and separate fractions, in a household furnace, as compared to conventional (CF) and alternative (AF) fuels. We demonstrated that each type of fuel (AF, CF, AFw) combusted in a household furnace is a significant source of air pollutants, especially fine PM2.5 particles, whose concentrations exceeded the limit values (3.1-17.2 times for PM2.5 and 0.5-7.4 times for PM10). The combustion of MW in household furnaces generated higher levels of PM2.5 (up to 345 µg/m3) and PM10 (up to 369 µg/m3) than AF or CF, at the same time being a significant source of TVOC (up to 0.3 mg/m3) and HCHO (0.4 mg/m3). The analysis showed that according to the Polish and European classification, air quality (AQI) during the combustion of all the materials analyzed is very poor (n = 12) or extremely poor (n = 19). The combustion of such materials as polystyrene, rubber and upholstery foam in household furnaces generates drastically high health risk to local inhabitants. We found that the combustion of polystyrene generated the highest Cancer Risk (CR) values of 1.04E-01 (children) and 2.60E-02 (adults), exceeding the acceptable level multiple times (CR > 10-6). Inhalation exposure to very poor air quality can lead to health problems, such as disorders of the respiratory, cardiovascular and immune systems. Additional risk is posed by solid fuel combustion in rural areas, which may be a significant factor deteriorating the chemical condition of soils, especially those used for agricultural purposes.

Sources and health risks of heavy metals in kindergarten dust: The role of particle size

Particle size effects significantly impact the concentration and toxicity of heavy metals (HMs) in dust. Nevertheless, the differences in concentrations, sources, and risks of HMs in dust with different particle sizes are unclear. Therefore, guided by the definition of atmospheric particulate matter, dust samples with particle sizes under 1000 μm (DT1000), 100 μm (DT100), and 63 μm (DT63) from Beijing kindergartens were collected. The concentrations of HMs (e.g., Cd, Pb, Zn, Ni, Cr, Ba, Cu, V, Mn, Co, and Ti) in dust samples with different particle sizes were measured. Besides, the differences in HM concentrations, contamination levels, sources, and source-oriented health risks in dust samples of different particle sizes were systematically explored. The results show that the concentrations of Mn, V, Zn, and Cd gradually increase with decreasing dust particle sizes, the concentrations of Ba and Pb show a decreasing trend, and the concentrations of Cr, Cu, Ni, and Co display an increasing and then decreasing trend. The degree of contamination of HMs in dust of different particle sizes varies, with Cd being the most dominant contaminant. Compared with DT1000 and DT63, DT100 is the most polluted. In addition, the sources of HMs in DT1000, DT100, and DT63 become more single with decreasing particle size, which may be mainly due to the particle-size effect inducing the redistribution of HMs in different sources. Notably, the potential health risk is higher in DT100 than in DT1000 and DT63. The highest contribution of industrial sources to the health risk is found in DT100, which is mainly caused by highly toxic chromium (Cr). This work emphasizes the importance of considering particle size in risk assessment and pollution control, which can provide a theoretical basis for precise management of HMs pollution in dust.

High spatiotemporal resolution estimation and analysis of global surface CO concentrations using a deep learning model

Ambient carbon monoxide (CO) is a primary air pollutant that poses significant health risks and contributes to the formation of secondary atmospheric pollutants, such as ozone (O3). This study aims to elucidate global CO pollution in relation to health risks and the influence of natural events like wildfires. Utilizing artificial intelligence (AI) big data techniques, we developed a high-performance Convolutional Neural Network (CNN)-based Residual Network (ResNet) model to estimate daily global CO concentrations at a high spatial resolution of 0.07° from June 2018 to May 2021. Our model integrated the global TROPOMI Total Column of atmospheric CO (TCCO) product and reanalysis datasets, achieving desirable estimation accuracies with R-values (correlation coefficients) of 0.90 and 0.96 for daily and monthly predictions, respectively. The analysis reveals that the CO concentrations were relatively high in northern and central China, as well as northern India, particularly during winter months. Given the significant role of wildfires in increasing surface CO levels, we examined their impact in the Indochina Peninsula, the Amazon Rain Forest, and Central Africa. Our results show increases of 60.0%, 28.7%, and 40.8% in CO concentrations for these regions during wildfire seasons, respectively. Additionally, we estimated short-term mortality cases related to CO exposure in 17 countries for 2019, with China having the highest mortality cases of 23,400 (95% confidence interval: 0-99,500). Our findings highlight the critical need for ongoing monitoring of CO levels and their health implications. The daily surface CO concentration dataset is publicly available and can support future relevant sustainable studies, which is accessible at https://doi.org/10.5281/zenodo.11806178.

Spatio-temporal variation of particulate matter with health impact assessment and long-range transport - case study: Ankara, Türkiye

A clean atmosphere should be provided as a right for human beings to live. The reality is that a significant proportion of the population is exposed to air pollution. This study presents an in-depth investigation into the spatio-temporal dynamics of PM2.5 concentrations in Ankara, Türkiye, spanning over three years. With particular emphasis on the impact of COVID-19 lockdown measures and local air quality management strategies, data from eight air pollution monitoring stations were analyzed. The findings indicate a significant reduction in PM2.5 levels during lockdown periods, with an average decrease of 18 % observed across the city. Implementing the Ankara Provincial Clean Air Action Plan further contributed to a 9.1 % decrease in PM2.5 concentrations in 2021, followed by an additional 6.6 % decrease in 2022 compared to 2020. The spatial distribution of PM2.5 concentrations reveals the influence of industrial and urban areas on pollution levels. Potential Source Contribution Function (PSCF) and Concentration-Weighted Trajectory (CWT) methods were employed to investigate the spatial and temporal variation of long-range transport source regions contributing to the PM2.5 levels in Ankara. PSCF and CWT analyses revealed a decreasing trend in anthropogenic contribution to PM2.5 from 2020 to 2022. The AirQ+ model was employed to predict the long-term mortality rates attributable to PM2.5 across different monitoring stations. Based on the estimations, all stations' average estimated attributable proportion is 9.8 % (3.3 %-27.8 %). The results depict varying trends in estimated mortality rates, emphasizing the importance of targeted interventions to mitigate the public health risks arising from exposure to polluted air. Overall, the results of this study show significant measures for the development of effective clean air quality strategies can effectively change the direction of the adverse impact of air pollution on public health.

Assessment of genotoxicity of air pollution in urban areas using an integrated model of passive biomonitoring

Atmospheric pollution is a major public health issue and has become increasingly critical for human health. Urban atmospheric pollution is typically assessed through physicochemical indicators aligned with environmental legislation parameters, providing data on air quality levels. While the effects of pollution on sensitive organisms serve as a warning for public health decision-makers, there remains a need to explore the interpretation of environmental data on pollutants. The use of species adapted to urban environments as sentinels enables continuous and integrated monitoring of environmental pollution implications on biological systems. In this study, we investigated the use of the plant species Tradescantia pallida as a biomonitor to evaluate the genotoxic effects of atmospheric pollution under diverse vehicular traffic conditions. T. pallida was strategically planted at the leading urban intersections in Uberlândia, Brazil. During COVID-19 pandemic lockdowns, we compared indicators such as physical, biological, and traffic data at different intersections in residential and commercial zones. The reduction in vehicular traffic highlighted the sensitivity of plant species to changes in air and soil pollutants. T. pallida showed bioaccumulation of heavy metals Cd and Cr in monitored areas with higher traffic levels. Additionally, we established a multiple linear regression model to estimate genotoxicity using the micronucleus test, with chromium concentration in the soil (X1) and particulate matter (PM) in the atmosphere (X2) identified as the primary independent variables. Our findings provide a comprehensive portrait of the impact of vehicular traffic changes on PM and offer valuable insights for refining parameters and models of Environmental Health Surveillance.

Socioeconomic and sociodemographic correlations to COVID-19 variability in the United States in 2020

The COVID-19 pandemic provided an additional spotlight on the longstanding socioeconomic/health impacts of redlining and has added to the myriad of environmental justice issues, which has caused significant loss of life, health, and productive work. The Centers for Disease Control and Prevention (CDC) reports that a person with any selected underlying health conditions is more likely to experience severe COVID-19 symptoms, with more than 81% of COVID-19-related deaths among people aged 65 years and older. The effects of COVID-19 are not homogeneous across populations, varying by socioeconomic status, PM2.5 exposure, and geographic location. This variability is supported by analysis of existing data as a function of the number of cases and deaths per capita/1,00,000 persons. We investigate the degree of correlation between these parameters, excluding health conditions and age. We found that socioeconomic variables alone contribute to ~40% of COVID-19 variability, while socioeconomic parameters, combined with political affiliation, geographic location, and PM2.5 exposure levels, can explain ~60% of COVID-19 variability per capita when using an OLS regression model; socioeconomic factors contribute ~28% to COVID-19-related deaths. Using spatial coordinates in a Random Forest (RF) regressor model significantly improves prediction accuracy by ~120%. Data visualization products reinforce the fact that the number of COVID-19 deaths represents 1% of COVID-19 cases in the US and globally. A larger number of democratic voters, larger per-capita income, and age >65 years is negatively correlated (associated with a decrease) with the number of COVID cases per capita. Several distinct regions of negative and positive correlations are apparent, which are dominated by two major regions of anticorrelation: (1) the West Coast, which exhibits high PM2.5 concentrations and fewer COVID-19 cases; and (2) the middle portion of the US, showing mostly high number of COVID-19 cases and low PM2.5 concentrations. This paper underscores the importance of exercising caution and prudence when making definitive causal statements about the contribution of air quality constituents (such as PM2.5) and socioeconomic factors to COVID-19 mortality rates. It also highlights the importance of implementing better health/lifestyle practices and examines the impact of COVID-19 on vulnerable populations, particularly regarding preexisting health conditions and age. Although PM2.5 contributes comparable deaths (~7M) per year, globally as smoking cigarettes (~8.5M), quantifying any causal contribution toward COVID-19 is non-trivial, given the primary causes of COVID-19 death and confounding factors. This becomes more complicated as air pollution was reduced significantly during the lockdowns, especially during 2020. This statistical analysis provides a modular framework, that can be further expanded with the context of multilevel analysis (MLA). This study highlights the need to address socioeconomic and environmental disparities to better prepare for future pandemics. By understanding how factors such as socioeconomic status, political affiliation, geographic location, and PM2.5 exposure contribute to the variability in COVID-19 outcomes, policymakers and public health officials can develop targeted strategies to protect vulnerable populations. Implementing improved health and lifestyle practices and mitigating environmental hazards will be essential in reducing the impact of future public health crises on marginalized communities. These insights can guide the development of more resilient and equitable health systems capable of responding effectively to similar future scenarios.

Geo-visualizing the hotspots of smog-induced health effects in district Gujranwala, Pakistan: a community perspective

This study aims to examine the health effects of smog on different age groups in Gujranwala and its associated health effects. To achieve this, primary data was gathered through a questionnaire survey focused on health issues faced by elderly individuals during the smog season. The results of the survey revealed that older adults in Gujranwala are particularly vulnerable to a range of health problems during this period, including coughing, throat infections, irritated eyes, runny noses, shortness of breath, chest pain while breathing, wheezing, asthma, heart problems, and respiratory issues. In order to analyze the spatial distribution of these health concerns, spatial and geo-statistical methods were employed utilizing ArcGIS 10.5. By integrating field data and secondary sources, hotspot and cold spot zones were identified. Employing the statistical model within ArcMap 10.5, hotspot analysis was performed to determine areas with elevated air quality index (AQI) values and associated health problems. The application of the inverse distance weighted approach, incorporating the Z value, facilitated a visual representation of areas with heightened and reduced AQI and health-related issues. The study's outcomes underscore the prevalence of health challenges among older adults during the winter months in Gujranwala, particularly linked to smog-induced throat infections, irritated eyes, and runny noses. The research identified zones with escalated AQI values, encompassing regions such as Gujranwala, Chandaqella, Alam Chowk, Khali Shahpur, Sialkot Bypass, and Pindi Bypass. It was established that industrial pollutants and vehicular emissions are the primary contributors to smog in the area. Given the detrimental consequences of pollution on individuals of all age groups, it is imperative to take action to mitigate its impact. This can be achieved through addressing pollution sources, implementing effective emission control measures, and fostering public awareness. By adopting proactive measures, the adverse health effects of pollution can be minimized, thereby fostering a healthier and safer environment for the entire population. This study offers valuable insights for policymakers and environmentalists to implement targeted interventions and improve air quality, ultimately safeguarding the health of local populations.

Investigating seasonal air quality variations consequent to the urban vegetation in the metropolis of Faisalabad, Pakistan

Urban atmospheric pollution is global problem and and have become increasingly critical in big cities around the world. Issue of toxic emissions has gained significant attention in the scientific community as the release of pollutants into the atmosphere rising continuously. Although, the Pakistani government has started the Pakistan Clean Air Program to control ambient air quality however, the desired air quality levels are yet to be reached. Since the process of mapping the dispersion of atmospheric pollutants in urban areas is intricate due to its dependence on multiple factors, such as urban vegetation and weather conditions. Therefore, present research focuses on two essential items: (1) the relationship between urban vegetation and atmospheric variables (temperature, relative humidity (RH), sound intensity (SI), CO, CO2, and particulate matter (PM0.5, PM1.0, and PM2.5) and (2) the effect of seasonal change on concentration and magnitude of atmospheric variables. A geographic Information System (GIS) was utilized to map urban atmospheric variables dispersion in the residential areas of Faisalabad, Pakistan. Pearson correlation and principal component analyses were performed to establish the relationship between urban atmospheric pollutants, urban vegetation, and seasonal variation. The results showed a positive correlation between urban vegetation, metrological factors, and most of the atmospheric pollutants. Furthermore, PM concentration showed a significant correlation with temperature and urban vegetation cover. GIS distribution maps for PM0.5, PM1.0, PM2.5, and CO2 pollutants showed the highest concentration of pollutants in poorly to the moderated vegetated areas. Therefore, it can be concluded that urban vegetation requires a rigorous design, planning, and cost-benefit analysis to maximize its positive environmental effects.

Exploring spatiotemporal pattern in the association between short-term exposure to fine particulate matter and COVID-19 incidence in the continental United States: a Leroux-conditional-autoregression-based strategy

Background

Numerous studies have demonstrated that fine particulate matter (PM2.5) is adversely associated with COVID-19 incidence. However, few studies have explored the spatiotemporal heterogeneity in this association, which is critical for developing cost-effective pollution-related policies for a specific location and epidemic stage, as well as, understanding the temporal change of association between PM2.5 and an emerging infectious disease like COVID-19.

Methods

The outcome was state-level daily COVID-19 cases in 49 native United States between April 1, 2020 and December 31, 2021. The exposure variable was the moving average of PM2.5 with a lag range of 0-14 days. A latest proposed strategy was used to investigate the spatial distribution of PM2.5-COVID-19 association in state level. First, generalized additive models were independently constructed for each state to obtain the rough association estimations, which then were smoothed using a Leroux-prior-based conditional autoregression. Finally, a modified time-varying approach was used to analyze the temporal change of association and explore the potential causes spatiotemporal heterogeneity.

Results

In all states, a positive association between PM2.5 and COVID-19 incidence was observed. Nearly one-third of these states, mainly located in the northeastern and middle-northern United States, exhibited statistically significant. On average, a 1 μg/m3 increase in PM2.5 concentration led to an increase in COVID-19 incidence by 0.92% (95%CI: 0.63-1.23%). A U-shaped temporal change of association was examined, with the strongest association occurring in the end of 2021 and the weakest association occurring in September 1, 2020 and July 1, 2021. Vaccination rate was identified as a significant cause for the association heterogeneity, with a stronger association occurring at a higher vaccination rate.

Conclusion

Short-term exposure to PM2.5 and COVID-19 incidence presented positive association in the United States, which exhibited a significant spatiotemporal heterogeneity with strong association in the eastern and middle regions and with a U-shaped temporal change.

Divulging the dust: An examination of particle deposition on soft ocular lens during urban commuting

Air pollution affecting the eye is a relatively new, emerging area of research that has implications for urban commuting and is the key first study. This article emphasizes the importance of understanding the effects of particle deposition on the human eye using soft lenses and their exposure, as well as identifying the chemical, elemental composition, and morphology of particles when commuting over a period of 21-day period. In this study, the focus is on personal sampling with soft contact lenses (42% Hioxifilcon A, 58% H2O) to understand particle deposition on ocular along with cascade to understand cut-off size. Volunteers are used for five different modes, namely bus, open and closed car windows, pedestrian, and two-wheeler. The SEM results show that the morphology in buses, pedestrians and cars are denser, irregular, and nodular, with no or minimal interstitial pores, while the particles in two-wheelers appeared to be fibrous, thin, crystalline, and non-porous ranging from 51.2 nm to 406.3 nm. The ICPMS results show the higher concentration compositions for different commuter types, namely: zinc (0.0562 μg/m3 and 0.1076 μg/m3) for buses and pedestrians, potassium (1.5013 μg/m3) and calcium (2.5892 μg/m3), magnesium (2.978 μg/m3), potassium (4.197 μg/m3), calcium (22.335 μg/m3) and iron (7.526 μg/m3) for two-wheelers. The organic elemental composition from FTIR predominant groups namely carbonyl, carboxylic, OH, N-H, C-H, CC, CO, and C-O. The experiment concludes that travellers in two-wheelers and pedestrians are more susceptible to particle deposits which leads to several ocular effects such as eye-irritation, dryness, and visual impairment.

Microplastics distribution in river side bars: The combined effects of water level and wind intensity

Rivers are the main pathway for microplastics (MP) transport toward the ocean. However, the understanding of the processes involved in the deposition and mobilization of MP in rivers, specifically in sediment side bars (SB), remains very limited. The objectives of this study were: (i) to examine the effect of hydrometric fluctuations and wind intensity on the distribution of microplastics (MP < 5 mm) in the SB of large river (the Paraná River), (ii) to determine the characteristics of MP to infer their origin and fate, and (iii) to discuss potential similarities or differences between MP suspended in the water column and MP found in sediment. The SB and water column were sampled during the autumn, winter, and spring of 2018, and the summer of 2019 at different river discharges and wind intensities. >90 % of the MP items found were fiber of polyethylene terephthalate (PET; FT-IR analysis), the most common MP color was blue, and most were in the 0.5-2 mm size range. The concentration/composition of MP varied according to the river discharge and wind intensity. During the falling limb of the hydrograph when discharge is decreasing and sediments are exposed for short periods (13-30 days), MP particles transported by the flow were deposited on temporarily exposed SB, accumulating there in high densities (309-373 items/kg). However, during the drought, when sediments remained exposed for a long time (259 days), MP were mobilized and transported by the wind. During this period (no influence of the flow), MP densities significantly decreased on SB (39-47 items/kg). In conclusion, both hydrological fluctuations and wind intensity played a significant role in MP distribution in SB.

Assessment of particulate matter exposure on ambient air and its impact on workers at two granite quarry mines at Njuli, Southern Malawi

Quarrying activities are among the significant sources of pollution caused by particulate matter (PM) in ambient air. Besides affecting the environment, PM exposure is one of the leading causes of respiratory illnesses worldwide. The current study, unique to Malawi, aimed to understand the impact of particulate matter on ambient air and the possible effects on workers' health at Njuli in Blantyre and Chiradzulu districts. The study measured concentrations of particulate matter in ambient air, conducted a chemical analysis of fallen dust, and surveyed respiratory symptoms. Results showed higher exposures to particulate matter at a Terrastone mine (126 µg/m3 highest exposure) than a Mota Engil mine (83 µg/m3 highest exposure), attributed to poor management practices at Terrastone mine. Higher PM readings (0-85.98 µg/m3) were observed for sites located downwind of the mines than sites located upwind (0-59 µg/m3), demonstrating the impact of quarrying operations at the mines; differences were, however, not statistically significant (P 0.165, Terrastone, P 0.678 Mota Engil). The observed amount of PM in ambient air was consistent with the scale of quarrying and processing operations at both mines, 0-56.2 µg/m3 in pre-operational phases, 0-126.1 µg/m3 during operations, and 0-56 µg/m3 after closure of operations; differences were statistically significant only at Terrastone mine P 0.003. The impact of season variation was observed following lower PM readings recorded during the rainy season than those obtained during the dry season at both mines. Analysis of health symptoms revealed that a higher proportion of workers at the Terrastone mine experienced respiratory symptoms compared to the Mota Engil mine.

Association Between Long-Term Exposure to Fine Particulate Matter Constituents and Progression of Cerebral Blood Flow Velocity in Beijing: Modifying Effect of Greenness

Few studies have explored the effects of fine particulate matter (PM2.5) and its constituents on the progression of cerebral blood flow velocity (BFV) and the potential modifying role of greenness. In this study, we investigated the association of PM2.5 and its constituents, including sulfate (SO4 2-), nitrate (NO3 -), ammonium (NH4 +), organic matter (OM), and black carbon (BC), with the progression of BFV in the middle cerebral artery. Participants from the Beijing Health Management Cohort who underwent at least two transcranial Doppler sonography examinations during 2015-2020 were recruited. BFV change and BFV change rate were used to define the progression of cerebral BFV. Linear mixed effects models were employed to analyze the data, and the weighted quantile sum regression assessed the contribution of PM2.5 constituents. Additionally, greenness was examined as a modifier. Among the examined constituents, OM exhibited the strongest association with BFV progression. An interquartile range increase in PM2.5 and OM exposure concentrations was associated with a decrease of -16.519 cm/s (95% CI: -17.837, -15.201) and -15.403 cm/s (95% CI: -16.681, -14.126) in BFV change, and -10.369 cm/s/year (95% CI: -11.387, -9.352) and -9.615 cm/s/year (95% CI: -10.599, -8.632) in BFV change rate, respectively. Furthermore, stronger associations between PM2.5 and BFV progression were observed in individuals working in areas with lower greenness, those aged under 45 years, and females. In conclusion, reducing PM2.5 levels in the air, particularly the OM constituent, and enhancing greenness could potentially contribute to the protection of cerebrovascular health.

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.

Air pollution in Iran: The current status and potential solutions

Air pollution has been integrated into global challenges over the last few years due to its negative impact on the health of human beings, increasing socio-economic risks, and its contribution to climate change. This study attempts to evaluate the current status of Iran's air pollution with regard to the sources of emissions, control policies, and the health and climate consequences that have resulted through available data from monitoring stations reported in the literature, official documents, and previously published papers. Many large cities in Iran surpass the permissible concentration of air pollutants, particularly particulate matter, sulfur dioxide, black carbon, and ozone. Although regulations and policies are in place and enormous efforts are being made to address air pollution issues in the country, implementation and enforcement are not as effective as they could be. The significant challenges may be regarded as the inefficiency of regulation and supervision systems, the lack of air quality monitoring systems and technology, particularly in industrial cities rather than Tehran, and the lack of continual feedback and investigations on the efficiency of regulation. Providing such an up-to-date report can bring opportunities for international collaboration, which is essential in addressing air pollution worldwide. We suggest that a way forward could be more focused on conducting systematic reviews using scientometric methods to show an accurate picture and trend in air pollution and its association in Iran, implementing an integrated approach for both climate change and air pollution issues, collaborating with international counterparts to share knowledge, tools, and techniques.

Response of cross-correlations between high PM2.5 and O3 with increasing time scales to the COVID-19: different trends in BTH and PRD

The air pollution in China currently is characterized by high fine particulate matter (PM2.5) and ozone (O3) concentrations. Compared with single high pollution events, such double high pollution (DHP) events (both PM2.5 and O3 are above the National Ambient Air Quality Standards (NAAQS)) pose a greater threat to public health and environment. In 2020, the outbreak of COVID-19 provided a special time window to further understand the cross-correlation between PM2.5 and O3. Based on this background, a novel detrended cross-correlation analysis (DCCA) based on maximum time series of variable time scales (VM-DCCA) method is established in this paper to compare the cross-correlation between high PM2.5 and O3 in Beijing-Tianjin-Heibei (BTH) and Pearl River Delta (PRD). At first, the results show that PM2.5 decreased while O3 increased in most cities due to the effect of COVID-19, and the increase in O3 is more significant in PRD than in BTH. Secondly, through DCCA, the results show that the PM2.5-O3 DCCA exponents α decrease by an average of 4.40% and 2.35% in BTH and PRD respectively during COVID-19 period compared with non-COVID-19 period. Further, through VM-DCCA, the results show that the PM2.5-O3 VM-DCCA exponents [Formula: see text] in PRD weaken rapidly with the increase of time scales, with decline range of about 23.53% and 22.90% during the non-COVID-19 period and COVID-19 period respectively at 28-h time scale. BTH is completely different. Without significant tendency, its [Formula: see text] is always higher than that in PRD at different time scales. Finally, we explain the above results with the self-organized criticality (SOC) theory. The impact of meteorological conditions and atmospheric oxidation capacity (AOC) variation during the COVID-19 period on SOC state are further discussed. The results show that the characteristics of cross-correlation between high PM2.5 and O3 are the manifestation of the SOC theory of atmospheric system. Relevant conclusions are important for the establishment of regionally targeted PM2.5-O3 DHP coordinated control strategies.

Statistical Analysis for Understanding PM2.5 Air Quality and the Impacts of COVID-19 Social Distancing in Several Provinces and Cities in Vietnam

Air pollution, especially in urban regions, is receiving increasing attention in Vietnam. Consequently, this work aimed to study and analyze the air quality in several provinces and cities in the country focusing on PM_2.5. Moreover, the impacts of COVID-19 social distancing on the PM_2.5 level were investigated. For this purpose, descriptive statistic, Box and Whisker plot, correlation matrix, temporal variation, and trend analysis were conducted. R-based program and the R package "openair" were employed for the calculations. Hourly PM_2.5 data were obtained from 8 national air quality monitoring sites. The study results indicated that provinces and cities in the North experienced more PM_2.5 pollution compared to the Central and South. PM_2.5 concentrations at each monitoring site varied significantly. Among monitoring sites, the northern sites showed high PM_2.5 correlations with each other than the other sites. Seasonal variation was observed with high PM_2.5 concentration in the dry season and low PM_2.5 concentration in the wet season. PM_2.5 concentration variation during the week was not so different. Diurnal variation showed that PM_2.5 concentration rose at peak traffic hours and dropped in the afternoon. There was mainly a decreasing trend in PM_2.5 concentration over the studied period. The COVID-19 pandemic has contributed to PM_2.5 reduction. In the months implemented social distancing for preventing the epidemic, PM_2.5 concentration declined but it would mostly increase in the following months. This study provided updated and valuable assessments of recent PM_2.5 air quality in Vietnam.

Temporal and Spatial Variations of Satellite-Based Aerosol Optical Depths, Angstrom Exponent, Single Scattering Albedo, and Ultraviolet-Aerosol Index over Five Polluted and Less-Polluted Cities of Northern India: Impact of Urbanization and Climate Change

It is widely acknowledged that factors such as population growth, urbanization's quick speed, economic growth, and industrialization all have a role in the atmosphere's rising aerosol concentration. In the current work, we assessed and discussed the findings of a thorough analysis of the temporal and spatial variations of satellite-based aerosol optical parameters such as Aerosol Optical Depth (AOD), Angstrom Exponent (AE), Single Scattering Albedo (SSA), and Ultraviolet-Aerosol Index (UV-AI), and their concentration have been investigated in this study over five polluted and less-polluted cities of northern India during the last decade 2011–2020. The temporal variation of aerosol optical parameters for AOD ranging from 0.2 to 1.8 with decadal mean 0.86 ± 0.36 for Patna region shows high value with a decadal increasing trend over the study area due to rise in aerosols combustion of fossil fuels, huge vehicles traffic, and biomass over the past ten years. The temporal variation of AE ranging from 0.3 to 1.8 with decadal mean 1.72 ± 0.11 for Agra region shows high value as compared to other study areas, which indicates a comparatively higher level of fine-mode aerosols at Agra. The temporal variation of SSA ranging from 0.8 to 0.9 with decadal mean 0.92 ± 0.02 for SSA shows no discernible decadal pattern at any of the locations. The temporal variation of UV-AI ranging from -1.01 to 2.36 with decadal mean 0.59 ± 0.06 for UV-AI demonstrates a rising tendency, with a noticeable rise in Ludhiana, which suggests relative dominance of absorbing dust aerosols over Ludhiana. Further, to understand the impact of emerging activities, analyses were done in seasonality. For this aerosol climatology was derived for different seasons, i.e., Winter, Pre-Monsoon, Monsoon, and Post-Monsoon. High aerosol was observed in Winter for the study areas Patna, Delhi, and Agra which indicated the particles major dominance of burning aerosol from biomass; and the worst in Monsoon and Post-Monsoon for the Tehri Garhwal and Ludhiana study areas which indicated most of the aerosol concentration is removed by rainfall. After that, we analyzed the correlation among all the parameters to better understand the temporal and spatial distribution characteristics of aerosols over the selected region. The value of r for AOD (550 nm) for regions 2 and 1(0.80) shows a strong positive correlation and moderately positive for the regions 3 and 1 (0.64), mostly as a result of mineral dust carried from arid western regions. The value of r for AE (412/470 nm) for region 3 and (0.40) shows a moderately positive correlation, which is the resultant of the dominance of fine-mode aerosol and negative for the regions 5 and 1 (− 0.06). The value of r for SSA (500 nm) for regions 2 and 1 (0.63) shows a moderately positive correlation, which explains the rise in big aerosol particles, which scatters sun energy more efficiently, and the value of r for UV-AI for regions 1 and 2 shows a strong positive correlation (0.77) and moderately positive for the regions 3 and 1 (0.46) which indicates the absorbing aerosols present over the study region.

Comparison of machine learning and deep learning techniques for the prediction of air pollution: a case study from China

The adverse effect of air pollution has always been a problem for human health. The presence of a high level of air pollutants can cause severe illnesses such as emphysema, chronic obstructive pulmonary disease (COPD), or asthma. Air quality prediction helps us to undertake practical action plans for controlling air pollution. The Air Quality Index (AQI) reflects the degree of concentration of pollutants in a locality. The average AQI was calculated for the various cities in China to understand the annual trends. Furthermore, the air quality index has been predicted for ten major cities across China using five different deep learning techniques, namely, Recurrent Neural Network (RNN), Bidirectional Gated Recurrent unit (Bi-GRU), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network BiLSTM (CNN-BiLSTM), and Convolutional BiLSTM (Conv1D-BiLSTM). The performance of these models has been compared with a machine learning model, eXtreme Gradient Boosting (XGBoost) to discover the most efficient deep learning model. The results suggest that the machine learning model, XGBoost, outperforms the deep learning models. While Conv1D-BiLSTM and CNN-BiLSTM perform well among the deep learning models in the estimation of the air quality index (AQI), RNN and Bi-GRU are the least performing ones. Thus, both XGBoost and neural network models are capable of capturing the non-linearity present in the dataset with reliable accuracy.