The Impact of COVID-19 Lockdown on Ambient Air Quality in Shanghai, 2022

The COVID-19 lockdown contributes to the improvement of air quality. Most previous studies have attributed this to the reduction of human activity while ignoring the meteorological changes, this may lead to an overestimation or underestimation of the impact of COVID-19 lockdown measures on air pollution levels. To investigate this issue, we propose an XGBoost-based model to predict the concentrations of PM2.5 and PM10 during the COVID-19 lockdown period in 2022, Shanghai, and thus explore the limits of anthropogenic emission on air pollution levels by comprehensively employing the meteorological factors and the concentrations of other air pollutants. Results demonstrate that actual observations of PM2.5 and PM10 during the COVID-19 lockdown period were reduced by 60.81% and 43.12% compared with the predicted values (regarded as the period without the lockdown measures). In addition, by comparing with the time series prediction results without considering meteorological factors, the actual observations of PM2.5 and PM10 during the lockdown period were reduced by 50.20% and 19.06%, respectively, against the predicted values during the non-lockdown period. The analysis results indicate that ignoring meteorological factors will underestimate the positive impact of COVID-19 lockdown measures on air quality. © 2023 by the authors.

Impact of Particulate Matter on Pre- and Post-Covid Lockdown in the Wards of Urban Jaipur

The impact of air pollutants on human health is a major issue that developing nations are facing in last decade. Effect of particulate matter especially PM2.5 and PM10 has been severely affecting mortality and morbidity in Rajasthan state as per global lead medical journal Lancet recent publication. Twelve air pollutant-monitoring sta tions in Jaipur city are not enough to predict the exact concentration of air pollutants in each of the 91 wards of Jaipur. In absence of accurate concentration at micro level, it becomes a major challenge for urban planners to remedial strategies. In this paper through GIS spatial distribution, a comparative study of particulate matter at each of the 91 wards during pre-lockdown for the year 2019 and post-lockdown 2020 was done. Results for 2020 showed slightly less pollution;similarly, air purity index, an indicator for healthier environment, was determined for each ward. © 2023 Scientific Publishers. All rights reserved.

Quantifying the impact of meteorological factors and green infrastructure location on particulate matter (PM) mitigation in North Macedonia using sensor collected data

Low quality of the air is becoming a major concern in urban areas. High values of particulate matter (PM) concentrations and various pollutants may be very dangerous for human health and the global environment. The challenge to overcome the problem with the air quality includes efforts to improve healthy air not only by reducing emissions, but also by modifying the urban morphology to reduce the exposure of the population to air pollution. The aim of this contribution is to analyse the influence of the green zones on air quality mitigation through sensor measurements, and to identify the correlation with the meteorological factors. Actually, the objective focuses on identifying the most significant correlation between PM2.5 and PM10 concentrations and the wind speed, as well as a negative correlation between the PM concentrations and wind speed across different measurement locations. Additionally, the estimation of slight correlation between the PM concentrations and the real feel temperature is detected, while insignificant correlations are found between the PM concentrations and the actual temperature, pressure, and humidity. In this paper the effect of the pandemic restriction rules COVID-19 lockdowns and the period without restriction are investigated. The sensor data collected before the pandemic (summer months in 2018), during the global pandemic (summer months 2020), and after the period with restriction measures (2022) are analysed.

Neighborhood-Level Particle Pollution Assessment during the COVID-19 Pandemic via a Novel IoT Solution

In recent years, the concentration levels of various air pollutants have been constantly increasing, primarily due to the high vehicle flow. In 2020, however, severe lockdowns in Greece were imposed to reduce the spread of the COVID-19 pandemic, which led to a rapid reduction in the concentration levels of air pollutants such as PM2.5 and PM10 in the atmosphere. Initially, this paper seeks to identify the correlation between the concentration levels of PM10 and the traffic flow by acquiring data from low-cost IoT devices which were placed in Thessaloniki, Greece, from March to August 2020. The correlation and the linearity between the two parameters were further investigated by applying descriptive analytics, regression techniques, Pearson correlation, and independent T-testing. The obtained results indicate that the concentration levels of PM10 are strongly correlated to the vehicle flow. Therefore, the results hint that the decrease in the vehicle flow could result in improving the quality of environmental air. Finally, the acquired results point out that the temperature and humidity are weakly correlated with the concentration levels of PM10 present in the atmosphere.

Future challenges and their impact on respiratory health and lung cancer

Air pollution, climate and population health are closely related in terms of their impacts on respiratory health and lung cancer. Air pollutants contribute to the exacerbation of chronic respiratory problems such as COPD and asthma. Air pollutants are also toxic and carcinogenic, initiating and promoting lung cancer development. Climate change in relation to environmental pollution affects the geographical distribution of food supply and diseases such as pneumonia in adults and children. The threat of air pollution, and hence global warming and climate changes, and their effects on population and respiratory health, is an imminent threat to the world and deserves immediate and sustainable combating strategies and efforts. The goals are to increase public awareness and engagement in action, with alignment of international collaboration and policy, and with steering towards further research. Now is the prime time for international collaborative efforts on planning and actions to fight air pollution and climate change before it is too late.Copyright © ERS 2021.

Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation

The unprecedented lockdown of human activities during the COVID-19 pandemic has significantly influenced social life in China. However, understanding the impact of this unique event on the emissions of different species is still insufficient, prohibiting the proper assessment of the environmental impacts of COVID-19 restrictions. Here we developed a multi-air-pollutant inversion system to simultaneously estimate the emissions of NOx, SO2, CO, PM2.5 and PM10 in China during COVID-19 restrictions with high temporal (daily) and horizontal (15 km) resolutions. Subsequently, contributions of emission changes versus meteorological variations during the COVID-19 lockdown were separated and quantified. The results demonstrated that the inversion system effectively reproduced the actual emission variations in multi-air pollutants in China during different periods of COVID-19 lockdown, which indicate that the lockdown is largely a nationwide road traffic control measure with NOx emissions decreasing substantially by ∼40 %. However, emissions of other air pollutants were found to only decrease by∼10% because power generation and heavy industrial processes were not halted during lockdown, and residential activities may actually have increased due to the stay-at-home orders. Consequently, although obvious reductions of PM2.5 concentrations occurred over the North China Plain (NCP) during the lockdown period, the emission change only accounted for 8.6 % of PM2.5 reductions and even led to substantial increases in O3. The meteorological variation instead dominated the changes in PM2.5 concentrations over the NCP, which contributed 90 % of the PM2.5 reductions over most parts of the NCP region. Meanwhile, our results suggest that the local stagnant meteorological conditions, together with inefficient reductions of PM2.5 emissions, were the main drivers of the unexpected PM2.5 pollution in Beijing during the lockdown period. These results highlighted that traffic control as a separate pollution control measure has limited effects on the coordinated control of O3 and PM2.5 concentrations under current complex air pollution conditions in China. More comprehensive and balanced regulations for multiple precursors from different sectors are required to address O3 and PM2.5 pollution in China.

Association between measured air pollution levels in the troposphere and surface air with COVID-19 hospitalization in Tomsk, Russia, 2022

The results of a preliminary analysis of the relationship between the short-term impact of air pollution exposure on hospitalizations associated with COVID-19 in Tomsk, Russia are presented. The statistical data on air pollution and COVID-19 associated hospitalization were collected and analyzed for the period from March 16, 2022 to April 14, 2022. This period corresponds to a flat plateau of confirmed COVID-19 cases after the main pandemic wave in 2022 in Tomsk and the Tomsk region which were associated with omicron strain of SARS-CoV-2. It was found that all representative peaks in a graph of daily hospitalizations coincide with the peaks in graphs of measured levels of air pollution. The increase in hospitalizations occurred on the same days when air pollution levels increased, or with a slight lag of 1-2 days. This allows us to tentatively conclude that air pollution has a quick effect on infected persons and may provoke an increase in symptoms and severity of the disease. Further detailed research is required. © 2022 SPIE.

An Exploratory Analysis of Delhi Air Quality Using Statistics and Machine Learning Models

Air pollution is one of the most significant concerns of the present era, which has severe and alarming effects on human health and the environment, thereby escalating the climate change issue. Hence, in-depth analysis of air pollution data and accurate air quality forecasting is crucial in controlling the growing pollution levels. It also aids in designing appropriate policies to prevent exposure to toxic pollutants and taking necessary precautionary measures. Air quality in Delhi, the capital of India, is inferior compared to other major cities in the world. In this study, daily and hourly concentrations of air pollutants in the Delhi region were collected and analyzed using various methods. A comparative analysis is performed based on months, seasons, and the topography of different stations. The effect of the Covid-19 lockdown on the reduction of pollutant levels is also studied. A correlation analysis is performed on the available data to show the relationships and dependencies among different pollutants, their relationship with weather parameters, and the correlations between the stations. Various machine learning models were used for air quality forecasting, like Linear Regression, Vector Auto Regression, Gradient Boosting Machine, Random Forest, and Decision Tree Regression. The performance of these models was compared using RMSE, MAE, and MAPE metrics. This study is focused on the dire state of air pollution in Delhi, the primary reasons behind it, and the efficacy of calculated lockdowns in bringing down pollution levels. It also highlights the potential of Linear Regression and Decision Tree Regression models in predicting the air quality for different time intervals. © 2022 IEEE.

Toxicological Effects of Fine Particulate Matter (PM2.5): Health Risks and Associated Systemic Injuries-Systematic Review.

Previous studies focused on investigating particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) have shown the risk of disease development, and association with increased morbidity and mortality rates. The current review investigate epidemiological and experimental findings from 2016 to 2021, which enabled the systemic overview of PM2.5's toxic impacts on human health. The Web of Science database search used descriptive terms to investigate the interaction among PM2.5 exposure, systemic effects, and COVID-19 disease. Analyzed studies have indicated that cardiovascular and respiratory systems have been extensively investigated and indicated as the main air pollution targets. Nevertheless, PM2.5 reaches other organic systems and harms the renal, neurological, gastrointestinal, and reproductive systems. Pathologies onset and/or get worse due to toxicological effects associated with the exposure to this particle type, since it can trigger several reactions, such as inflammatory responses, oxidative stress generation and genotoxicity. These cellular dysfunctions lead to organ malfunctions, as shown in the current review. In addition, the correlation between COVID-19/Sars-CoV-2 and PM2.5 exposure was also assessed to help better understand the role of atmospheric pollution in the pathophysiology of this disease. Despite the significant number of studies about PM2.5's effects on organic functions, available in the literature, there are still gaps in knowledge about how this particulate matter can hinder human health. The current review aimed to approach the main findings about the effect of PM2.5 exposure on different systems, and demonstrate the likely interaction of COVID-19/Sars-CoV-2 and PM2.5.

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

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

Attentive Multi-Task Prediction of Atmospheric Particulate Matter: Effect of the COVID-19 Pandemic

Air pollution, especially the continual increase in atmospheric particulate matter (PM), is a global environmental challenge. To reduce the PM concentration, a remarkable amount of machine learning-based research has been proposed. However, increasing the accuracy of the predictions and providing clear interpretations of the predictions are challenging. In particular, no studies have addressed models that predict and interpret PM before and during the COVID-19 pandemic. In this paper, we present a two-step predictive and explainable model to obtain insights into reducing PM. We first use attentive multi-task learning to predict the air quality of cities. To accurately predict the concentration of particles with sizes of similar to 10 mu m or similar to 2.5 mu m (PM10 and PM2.5, respectively), we demonstrate a performance difference between single-task and multi-task learning, as well as among the state-of-the art methods. The proposed attentive model with multi-task learning outperformed the others in terms of accuracy performance. We then used Shapley additive explanations, a representative explainable artificial intelligence framework, to interpret and determine the significance of features for predicting PM10 and PM2.5. We demonstrated the superiority of the proposed approach in predicting and explaining both PM10 and PM2.5 concentrations, and observed a statistically significant difference in air pollution before and during the COVID-19 pandemic.

PM2.5-bound trace metals in an urban area of Northern Mexico during the COVID-19 pandemic: characterization, sources, and health risk.

Abstract: A field study was carried out in the Metropolitan Area of Monterrey (MAM), the second most populated city in Mexico, characterized by increasing urbanization, high traffic density, and intense industrial activity. These characteristics commonly present high concentrations of air pollutants leading to the degradation of air quality. PM2.5 was analyzed for heavy metals at two urban sites located within the MAM (Juarez and San Bernabe) in order to determine sources, health risk, morphology, and elemental content during the COVID-19 pandemic (autumn 2020 and spring 2021). Twenty-four-hour samples of PM2.5 were collected at each site during 30-day periods using high-volume equipment. Gravimetric concentrations and 11 metals were measured (Ca, Cd, Co, Cu, Fe, K, Mg, Mn, Ni, Cr, and Pb) by different analytical techniques (flame atomic absorption spectroscopy, graphite furnace atomic absorption spectroscopy, and inductively coupled plasma optical emission spectroscopy). Selected samples were analyzed by scanning electron microscopy-energy-disperse spectroscopy in order to characterize their morphology and elemental content. PM2.5 concentrations exceeded the Mexican standard and WHO guidelines in Juarez during spring 2021. Cu, Cd, and Co were highly enriched by anthropogenic sources, and Ni, K, Cr, and Pb had a moderate enrichment. Mg, Mn, and Ca were of crustal origin. Bivariate statistics and PCA confirmed that alkaline metals originated from crustal sources and that the main sources of trace metals included traffic emissions, resuspension from soil/road dust, steel industry, smelting, and non-exhaust emissions at both sites. Lifetime cancer risk coefficients did not exceed the permissible levels established by EPA and WHO, implying that local residents are not at risk of developing cancer. Non-carcinogenic risk coefficients revealed that there is a possible risk of suffering cardiovascular and respiratory diseases due to inhalation of cobalt at the study sites. Supplementary Information: The online version contains supplementary material available at 10.1007/s11869-023-01372-7.

Assessing the effectiveness of portable HEPA air cleaners for reducing particulate matter exposure in King County, Washington homeless shelters: Implications for community congregate settings.

Over four thousand portable air cleaners (PACs) with high-efficiency particulate air (HEPA) filters were distributed by Public Health - Seattle & King County to homeless shelters during the COVID-19 pandemic. This study aimed to evaluate the real-world effectiveness of these HEPA PACs in reducing indoor particles and understand the factors that affect their use in homeless shelters. Four rooms across three homeless shelters with varying geographic locations and operating conditions were enrolled in this study. At each shelter, multiple PACs were deployed based on the room volume and PAC's clean air delivery rate rating. The energy consumption of these PACs was measured using energy data loggers at 1-min intervals to allow tracking of their use and fan speed for three two-week sampling rounds, separated by single-week gaps, between February and April 2022. Total optical particle number concentration (OPNC) was measured at 2-min intervals at multiple indoor locations and an outdoor ambient location. The empirical indoor and outdoor total OPNC were compared for each site. Additionally, linear mixed-effects regression models (LMERs) were used to assess the relationship between PAC use time and indoor/outdoor total OPNC ratios (I/OOPNC). Based on the LMER models, a 10 % increase in the hourly, daily, and total time PACs were used significantly reduced I/OOPNC by 0.034 [95 % CI: 0.028, 0.040; p < 0.001], 0.051 [95 % CI: 0.020, 0.078; p < 0.001], and 0.252 [95 % CI: 0.150, 0.328; p < 0.001], respectively, indicating that keeping PACs on resulted in significantly lower I/OOPNC. The survey suggested that keeping PACs on and running was the main challenge when operating them in shelters. These findings suggested that HEPA PACs were an effective short-term strategy to reduce indoor particle levels in community congregate living settings during non-wildfire seasons and the need for formulating practical guidance for using them in such an environment.

Co-exposure to urban particulate matter and aircraft noise adversely impacts the cerebro-pulmonary-cardiovascular axis in mice

Worldwide, up to 8.8 million excess deaths/year have been attributed to air pollution, mainly due to the exposure to fine particulate matter (PM). Traffic-related noise is an additional contributor to global mortality and morbidity. Both health risk factors substantially contribute to cardiovascular, metabolic and neuropsychiatric sequelae. Studies on the combined exposure are rare and urgently needed because of frequent co-occurrence of both risk factors in urban and industrial settings. To study the synergistic effects of PM and noise, we used an exposure system equipped with aerosol generator and loud-speakers, where C57BL/6 mice were acutely exposed for 3d to either ambient PM (NIST particles) and/or noise (aircraft landing and take-off events). The combination of both stressors caused endothelial dysfunction, increased blood pressure, oxidative stress and inflammation. An additive impairment of endothelial function was observed in isolated aortic rings and even more pronounced in cerebral and retinal arterioles. The increase in oxidative stress and inflammation markers together with RNA sequencing data indicate that noise particularly affects the brain and PM particularly affects the lungs. Noise also increased levels of circulating stress hormones adrenaline and noradrenaline, while PM increased levels of circulating cytokines CD68 and MCP-1. The combination of both stressors has additive adverse effects on the cardiovascular system that are based on PM-induced systemic inflammation and noise-triggered stress hormone signaling. We demonstrate an additive upregulation of ACE-2 in the lung, suggesting that there may be an increased vulnerability to COVID-19 infection. The data warrant further mechanistic studies to characterize the propagation of primary target tissue damage (lung, brain) to remote organs such as aorta and heart by combined noise and PM exposure.Copyright © 2023

Air quality changes in Shijiazhuang during COVID-19 outbreak

[Background] The coronavirus disease 2019 (COVID-19) was first detected in December 2019. To combat the disease, a series of strict measures were adopted across the country, which led of improved air quality. This provides an opportunity to discuss the impact of human activities on air quality. [Objective] This study investigates the air quality changes in Shijiazhuang, and analyzes the impacts of epidemic prevention and control measures on air quality, so as to provide reference and ideas for further improving air quality and prevention and control measures. [Methods] The air quality data were collected online from https://www.zq12369.com/ and https://aqicn.org/city/shijiazhuang/cn/. Comparisons in air quality index (AQI) and the concentrations of air pollutants (PM2.5, PM10, SO2, CO, NO2, and O3) were made between the period from December 2019 to June 2020 (reference) and the same period from 2016 to 2019 by t-test and chi-square test. [Results] The daily average AQI dropped by 25.38% in Shijiazhuang during the COVID-19 prevention and control compared with the some period from 2016 to 2019 (t=6.28, P < 0.05). The proportions of pollution days during the COVID-19 outbreak in Shijiazhuang were PM2.5 (44.56%), O3 (31.09%), PM10 (23.83%), and NO2 (2.59%) successively, the pollution days of PM10 decreased significantly (chi2=3.86, P < 0.05) compared with 2016-2019, but during traffic lockdown the numbers of pollution days of PM2.5 and in the mid stage of prevention the number of pollution days of O3 increased (P < 0.05). Compared with the control period, the concentrations of the six air pollutants decreased to varying degrees (P < 0.05), especially SO2 dropped by 55.36%. [Conclusion] The measures taken for COVID-19 control and prevention have reduced the pollution sources and emissions, which resulted in better general air quality of Shijiazhuang City, but have aggravated the pollution of O3 and other pollutants. It is necessary to further explore the causes for the aggravation of O3 pollution in order to formulate reasonable air quality control strategies.Copyright © 2021, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.

Performance of in-duct bipolar ionization devices on pollutant removal and potential byproduct formation in indoor environments

The COVID-19 pandemic has highlighted the importance of indoor air quality (IAQ) since SARS-CoV-2 may be transmitted through virus-laden aerosols in poorly ventilated spaces. Multiple air cleaning technologies have been developed to mitigate airborne transmission risk and improve IAQ. In-duct bipolar ionization technology is an air cleaning technology that can generate ions for inactivating airborne pathogens and increasing particle deposition and removal while without significant byproducts generated. Many commercial in-duct ionization systems have been developed but their practical performance on pollutant removal and potential formation of byproducts have not been investigated comprehensively. The results in this study showed that the in-duct bipolar ionization technology can significantly improve the particle removal efficiency of the regular filter, while no significant ozone and ion were released to the indoor air. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Characterization and Source Apportionment of PM in Handan—A Case Study during the COVID-19

Handan is a typical city affected by regional particulate pollution. In order to investigate particulate matter (PM) characterization, source contributions and health risks for the general populations, we collected PM samples at two sites affected by a pollution event (12–18 May 2020) during the COVID-19 pandemic and analyzed the major components (SNA, OCEC, WSIIs, and metal elements). A PCA-MLR model was used for source apportionment. The carcinogenic and non-carcinogenic risks caused by metal elements in the PM were assessed. The results show that the renewal of old neighborhoods significantly influences local PM, and primarily the PM10;the average contribution to PM10 was 27 μg/m3. The source apportionment has indicated that all other elements came from dust, except Cd, Pb and Zn, and the contribution of the dust source to PM was 60.4%. As PM2.5 grew to PM10, the PM changed from basic to acidic, resulting in a lower NH4+ concentration in PM10 than PM2.5. The carcinogenic risk of PM10 was more than 1 × 10−6 for both children and adults, and the excess mortality caused by the renewal of the community increased by 23%. Authorities should pay more attention to the impact of renewal on air quality. The backward trajectory and PSCF calculations show that both local sources and short-distance transport contribute to PM—local sources for PM10, and short-distance transport in southern Hebei, northern Henan and northern Anhui for PM2.5, SO2 and NO2. © 2023 by the authors.

Air pollution with pm10 for the municipality of Burgas and the monthly cases of disease with COVID 19, 2021

An important environmental problem for the Municipality of Burgas is the relatively high levels of PM10 pollution. Particulate matter PM10 is defined as the fraction of particles with an aerodynamic diameter smaller than 10 pm. The article provides statistical processing and evaluation of daily data on the concentration of PM10 in the air by quarters fix Burgas, 2021. A histogram of the frequency distribution of concentrations by quarters was prepared. A regression model for calculating the monthly concentrations in the atmospheric air is derived The tests and inspections performed show that the performed modelling is suitable for evaluation, analysis and forecast. Air pollution harms human health and the environment. Exposure ID air pollution is associated with a wide range of acute and chronic health effects, ranging from irritating effects to death From the end of 2019 until now in the world, Europe and in particular Bulgaria is raging a dangerous respiratory disease known as COVD19. The average monthly new cases of COVD19 for Burgas were assessed, as well as the respective maximum and minimum monthly values. A qualitative assessment of the relationship between the monthly concentrations of PM10 and the incidence of COVID19 was made.

Sources of water-soluble organic carbon in fine particles at a southern European urban background site

In this study, the temporal evolution and sources of water-soluble organic carbon (WSOC) in submicron particles at an urban background site in Elche (Spain) were investigated. Measurements of PM1 (N = 200) were carried out over one year (2021). Samples were analysed for organic carbon (OC), elemental carbon (EC), WSOC, levoglucosan, elements and major ions. A positive matrix factorization (PMF) analysis was performed in order to identify the sources of WSOC on an annual and a monthly basis. During the study period, traffic restrictions due to COVID-19 led to lower concentrations of PM1 and carbonaceous compounds than expected. The WSOC annual average mass concentration was 0.95 mugm-3, with maximum values during the colder months. The apportionment results indicate that the biomass burning (BB) source contributed 30.63% to WSOC levels, road traffic (RT) accounted for 23.90% of the WSOC, while the contribution of a source related to secondary organic aerosol formation (ammonium sulfate-AS) was 33.80%. Minor sources of WSOC were: soil dust (SD) and secondary nitrate (SN), which contributed 7.44% and 4.22%, respectively, to WSOC concentrations. The WSOC/OC ratio did not exhibit significant variations during the study period, since source contributions were similar for WSOC and OC. The highest values of this ratio were recorded in summer, due to the higher contribution from the AS source to WSOC concentrations.Copyright © 2023 The Authors

Particulate matter air pollution and COVID-19 risk: A systematic review

Introduction/Aim: Ecological studies indicate ambient particulate matter >=2.5 mm (PM 2.5) air pollution is associated with poorer COVID-19 outcomes. However, these studies cannot account for individual heterogeneity and often lack precision in estimates of PM 2.5 exposure. We summarise evidence relating on individual-level data to determine whether PM 2.5 exposure increases the risk of COVID-19 infection, severe disease and death. Method(s): We conducted a systematic review of relevant case-control and cohort studies, searching Medline, Embase and the WHO COVID-19 databases. Study quality was evaluated using the Newcastle-Ottawa Scale. Result(s): N = 18 studies met the inclusion criteria. Generally, PM 2.5 exposure was significantly associated with higher rates of COVID-19 infection (all 7 studies positive) and severe COVID-19 disease (8 of 9 studies positive, 1 null). The effects on mortality were mixed but indicative of a positive association (4 of 6 studies positive, 2 null). Most studies were rated 'good' quality (13 of 18 studies), though there were still methodological issues;few used individual-level data to adjust for important confounders like socioeconomic status (3 of 18 studies), instead using area-based indicators (12 of 18 studies) or not adjusting for it at all (3 of 18 studies). Most studies with severe disease (9 of 10 studies) and mortality outcomes (5 of 6 studies) were based on people already diagnosed COVID-19, potentially introducing collider bias. Conclusion(s): There is strong evidence that ambient particulate matter air pollution increases the risk of COVID-19 infection, and weaker evidence of increases in risk of severe disease and mortality.