Critical air pollutant assessments and health effects attributed to PM2.5 during and after COVID-19 lockdowns in Iran: application of AirQ+ models.

Objectives: The aim of this study was to evaluate changes in air quality index (AQI) values before, during, and after lockdown, as well as to evaluate the number of hospitalizations due to respiratory and cardiovascular diseases attributed to atmospheric PM2.5 pollution in Semnan, Iran in the period from 2019 to 2021 during the COVID-19 pandemic. Methods: Daily air quality records were obtained from the global air quality index project and the US Environmental Protection Administration (EPA). In this research, the AirQ+ model was used to quantify health consequences attributed to particulate matter with an aerodynamic diameter of <2.5 µm (PM2.5). Results: The results of this study showed positive correlations between air pollution levels and reductions in pollutant levels during and after the lockdown. PM2.5 was the critical pollutant for most days of the year, as its AQI was the highest among the four investigated pollutants on most days. Mortality rates from chronic obstructive pulmonary disease (COPD) attributed to PM2.5 in 2019-2021 were 25.18% in 2019, 22.55% in 2020, and 22.12% in 2021. Mortality rates and hospital admissions due to cardiovascular and respiratory diseases decreased during the lockdown. The results showed a significant decrease in the percentage of days with unhealthy air quality in short-term lockdowns in Semnan, Iran with moderate air pollution. Natural mortality (due to all-natural causes) and other mortalities related to COPD, ischemic heart disease (IHD), lung cancer (LC), and stroke attributed to PM2.5 in 2019-2021 decreased. Conclusion: Our results support the general finding that anthropogenic activities cause significant health threats, which were paradoxically revealed during a global health crisis/challenge.

Impact of Covid-19 pandemic lockdown on the urban litter and clean environment index.

Changing the level of pollution in the urban environment is one of the consequences of Covid-19. Litter are one of the most important urban pollutants affected by the Covid-19 pandemic. In this research, the pollution level of urban areas during the Covid-19 pandemic was investigated by studying the urban environment. To this end, the protocol of observation and counting was used and litter were studied in two groups including common litter and Covid-19 related litter in Yasuj, Iran. The results were interpreted based on the clean environment index (CEI). The time of observation was selected based on the peak of the disease and the decline in the incidence rate. The results showed that on average, at the peak of the disease, the density of the litter was reduced by 19% compared to the low lockdown related to Covid-19. The CEI on average was 4.76 at the peak of the disease that was interpreted in the clean status, while the CEI on average was 5.94 at the low lockdown related to Covid-19 so interpreted in the moderate status. Among urban land uses, recreational areas with a difference of more than 60% showed the greatest impact caused by Covid-19, while in commercial areas this difference was less than 3%. The effect of Covid-19 related litter on the calculated index was 73% in the worst case and 0.8% in the lowest case. Although Covid-19 decreased the number of litter in urban areas, the emergence of Covid-19 lockdown related litter was a cause for concern and led to increasing the CEI.

Phthalate esters: occurrence, toxicity, bioremediation, and advanced oxidation processes.

Phthalic acid esters are emerging pollutants, commonly used as plasticizers that are categorized as hazardous endocrine-disrupting chemicals (EDCs). A rise in anthropogenic activities leads to an increase in phthalate concentration in the environment which leads to various adverse environmental effects and health issues in humans and other aquatic organisms. This paper gives an overview of the research related to phthalate ester contamination and degradation methods by conducting a bibliometric analysis with VOS Viewer. Ecotoxicity analysis requires an understanding of the current status of phthalate pollution, health impacts, exposure routes, and their sources. This review covers five toxic phthalates, occurrences in the aquatic environment, toxicity studies, biodegradation studies, and degradation pathways. It highlights the various advanced oxidation processes like photocatalysis, Fenton processes, ozonation, sonolysis, and modified AOPs used for phthalate removal from the environment.

Pandemic COVID-19 ends but soil pollution increases: Impacts and a new approach for risk assessment.

For three years, a large amount of manufactured pollutants such as plastics, antibiotics and disinfectants has been released into the environment due to COVID-19. The accumulation of these pollutants in the environment has exacerbated the damage to the soil system. However, since the epidemic outbreak, the focus of researchers and public attention has consistently been on human health. It is noteworthy that studies conducted in conjunction with soil pollution and COVID-19 represent only 4 % of all COVID-19 studies. In order to enhance researchers' and the public awareness of the seriousness on the COVID-19 derived soil pollution, we propose the viewpoint that "pandemic COVID-19 ends but soil pollution increases" and recommend a whole-cell biosensor based new method to assess the environmental risk of COVID-19 derived pollutants. This approach is expected to provide a new way for environmental risk assessment of soils affected by contaminants produced from the pandemic.

Spatial variability of trace gases (NO2, O3 and CO) over Indian region during 2020 and 2021 COVID-19 lockdowns.

COVID-19 lockdown has given us an opportunity to investigate the pollutant concentrations in response to the restricted anthropogenic activities. The atmospheric concentration levels of nitrogen dioxide (NO2), carbon monoxide (CO) and ozone (O3) have been analysed for the periods during the first wave of COVID-19 lockdown in 2020 (25th March-31st May 2020) and during the partial lockdowns due to second wave in 2021 (25th March-15th June 2021) across India. The trace gas measurements from Ozone Monitoring Instrument (OMI) and Atmosphere InfraRed Sounder (AIRS) satellites have been used. An overall decrease in the concentration of O3 (5-10%) and NO2 (20-40%) have been observed during the 2020 lockdown when compared with business as usual (BAU) period in 2019, 2018 and 2017. However, the CO concentration increased up to 10-25% especially in the central-west region. O3 and NO2 slightly increased or had no change in 2021 lockdown when compared with the BAU period, but CO showed a mixed variation prominently influenced by the biomass burning/forest fire activities. The changes in trace gas levels during 2020 lockdown have been predominantly due to the reduction in the anthropogenic activities, whereas in 2021, the changes have been mostly due to natural factors like meteorology and long-range transport, as the emission levels have been similar to that of BAU. Later phases of 2021 lockdown saw the dominant effect of rainfall events resulting in washout of pollutants. This study reveals that partial or local lockdowns have very less impact on reducing pollution levels on a regional scale as natural factors like atmospheric long-range transport and meteorology play deciding roles on their concentration levels.

Assessment of light-duty versus heavy-duty diesel on-road mobile source emissions using general additive models applied to traffic volume and air quality data and COVID-19 responses.

Following the outbreak of the COVID-19 pandemic, several papers have examined the effect of the pandemic response on urban air pollution worldwide. This study uses observed traffic volume and near-road air pollution data for black carbon (BC), oxides of nitrogen (NOx), and carbon monoxide (CO) to estimate the emissions contributions of light-duty and heavy-duty diesel vehicles in five cities in the continental United States. Analysis of mobile source impacts in the near-road environment has several health and environmental justice implications. Data from the initial COVID-19 response period, defined as March to May in 2020, were used with data from the same period over the previous two years to develop general additive models (GAMs) to quantify the emissions impact of each vehicle class. The model estimated that light-duty traffic contributes 4-69%, 14-65%, and 21-97% of BC, NOx, and CO near-road levels, respectively. Heavy-duty diesel traffic contributes an estimated 26-46%, 17-63%, and -7-18% of near-road levels of the three pollutants. The estimated mobile source impacts were used to calculate NOx to CO and BC to NOx emission ratios, which were between 0.21-0.32 µg m-3 NOx (µg m-3 CO)-1 and 0.013-0.018 µg m-3 BC (µg m-3 NOx)-1. These ratios can be used to assess existing emission inventories for use in determining air pollution standards. These results agree moderately well with recent National Emissions Inventory estimates and other empirically-derived estimates, showing similar trends among the pollutants. However, a limitation of this study was the recurring presence of an implausible air pollution impact estimate in 41% of the site-pollutant combinations, where a vehicle class was estimated to account for either a negative impact or an impact higher than the total estimated pollutant concentration. The variations seen in the GAM estimates are likely a result of location-specific factors, including fleet composition, external pollution sources, and traffic volumes.Implications: Drastic reductions in traffic and air pollution during the lockdowns of the COVID-19 pandemic present a unique opportunity to assess vehicle emissions. A General Additive Modeling approach is developed to relate traffic levels, observed air pollution, and meteorology to identify the amount vehicle types contribute to near-road levels of traffic-related air pollutants (TRAPs), which is important for future emission regulation and policy, given the significant health and environmental justice implications of vehicle-related pollution along major roadways. The model is used to evaluate emission inventories in the near-road environment, which can be used to refine existing estimates. By developing a locally data-driven method to readily characterize impacts and distinguish between heavy and light duty vehicle effects, local regulations can be used to target policies in major cities around the country, thus addressing local health disbenefits and disparities occurring as a result of exposure to near-road air pollution.

Prediction of atmospheric pollutants in urban environment based on coupled deep learning model and sensitivity analysis.

Accurate and efficient predictions of pollutants in the atmosphere provide a reliable basis for the scientific management of atmospheric pollution. This study develops a model that combines an attention mechanism, convolutional neural network (CNN), and long short-term memory (LSTM) unit to predict the O3 and PM2.5 levels in the atmosphere, as well as an air quality index (AQI). The prediction results given by the proposed model are compared with those from CNN-LSTM and LSTM models as well as random forest and support vector regression models. The proposed model achieves a correlation coefficient between the predicted and observed values of more than 0.90, outperforming the other four models. The model errors are also consistently lower when using the proposed approach. Sobol-based sensitivity analysis is applied to identify the variables that make the greatest contribution to the model prediction results. Taking the COVID-19 outbreak as the time boundary, we find some homology in the interactions among the pollutants and meteorological factors in the atmosphere during different periods. Solar irradiance is the most important factor for O3, CO is the most important factor for PM2.5, and particulate matter has the most significant effect on AQI. The key influencing factors are the same over the whole phase and before the COVID-19 outbreak, indicating that the impact of COVID-19 restrictions on AQI gradually stabilized. Removing variables that contribute the least to the prediction results without affecting the model prediction performance improves the modeling efficiency and reduces the computational costs.

Endocrine disrupting micropollutants in water and their effects on human fertility and fecundity.

INTRODUCTION AND OBJECTIVE: Micropollutants (MPs) are defined as persistent and biologically-active substances which occur in the environment in trace amounts, mainly as a result of industrial processes and human domestic activity. The published experimental data prove that, among other things, MPs present in the environment may also affect and disturb hormonal balance in humans, resulting in impairment of the reproductive function. In addition to the many MPs disrupting endocrine function described in literature and which exert an effect on human reproductive function, the study presents a review of current literature concerning the exposure to Bisphenol A, phthalates, organochlorine pesticides, and pyrethroids. REVIEW METHODS: Two independent authors searched in PubMed and Google scholar (any date until September 2022) for studies concerning chosen endocrine-disrupting MPs in water and their effects on human fertility and fecundity. BRIEF DESCRIPTION OF THE STATE OF KNOWLEDGE: The review of the literature showed that EDMs present in the environment may create risk in the prenatal and postnatal development following premature birth, and exert a negative effect on fertility and reproductive functions in humans, especially during the perinatal period. SUMMARY: The presented review of literature indicates a negative effect of exposure to BPA, phthalates, OC and OP pesticides, as well as to pyrethroids, regarding human reproductive health. It also demonstrated considerable differences according to gender. Generally, there is a definitely stronger evidence for the presence of a cause-effect relationship between the discussed EDMs and a decreased fertility and fecundity in males. The negative effect of exposure to Bisphenol A, phthalates, selected organochlorine pesticides and pyrethroids appears to be quite well documented.

Removal Performance and Mechanism of Emerging Pollutant Chloroquine Phosphate from Water by Iron and Magnesium Co-Modified Rape Straw Biochar.

Chloroquine phosphate (CQP) is effective in treating coronavirus disease 2019 (COVID-19); thus, its usage is rapidly increasing, which may pose a potential hazard to the environment and living organisms. However, there are limited findings on the removal of CQP in water. Herein, iron and magnesium co-modified rape straw biochar (Fe/Mg-RSB) was prepared to remove CQP from the aqueous solution. The results showed that Fe and Mg co-modification enhanced the adsorption efficiency of rape straw biochar (RSB) for CQP with the maximum adsorption capacity of 42.93 mg/g (at 308 K), which was about two times higher than that of RSB. The adsorption kinetics and isotherms analysis, as well as the physicochemical characterization analysis, demonstrated that the adsorption of CQP onto Fe/Mg-RSB was caused by the synergistic effect of pore filling, π-π interaction, hydrogen bonding, surface complexation, and electrostatic interaction. In addition, although solution pH and ionic strength affected the adsorption performance of CQP, Fe/Mg-RSB still had a high adsorption capability for CQP. Column adsorption experiments revealed that the Yoon-Nelson model better described the dynamic adsorption behavior of Fe/Mg-RSB. Furthermore, Fe/Mg-RSB had the potential for repeated use. Therefore, Fe and Mg co-modified biochar could be used for the remediation of CQP from contaminated water.

Evaluation of indoor environmental quality, personal cumulative exposure dose, and aerosol transmission risk levels inside urban buses in Dalian, China.

The transmission of pollutants in buses has an important impact on personal exposure to airborne particles and spread of the COVID-19 epidemic in enclosed spaces. We conducted the following real-time field measurements inside buses: CO2, airborne particle concentration, temperature, and relative humidity data during peak and off-peak hours in spring and autumn. Correlation analysis was adopted to evaluate the dominant factors influencing CO2 and particle mass concentrations in the vehicle. The cumulative personal exposure dose to particulate matter and reproduction number were calculated for passengers on a one-way trip. The results showed the in-cabin CO2 concentrations, with 22.11% and 21.27% of the total time exceeding 1000 ppm in spring and autumn respectively. In-cabin PM2.5 mass concentration exceeded 35 µm/m3 by 57.35% and 86.42% in spring and autumn, respectively. CO2 concentration and the cumulative number of passengers were approximately linearly correlated in both seasons, with R value up to 0.896. The cumulative number of passengers had the most impact on PM2.5 mass concentration among tested parameters. The cumulative personal exposure dose to PM2.5 during a one-way trip in autumn was up to 43.13 µg. The average reproductive number throughout the one-way trip was 0.26; it was 0.57 under the assumed extreme environment. The results of this study provide an important basic theoretical guidance for the optimization of ventilation system design and operation strategies aimed at reducing multi-pollutant integrated health exposure and airborne particle infection (such as SARS-CoV-2) risks.

Dynamics of Microbial Community and Potential Microbial Pollutants in Shopping Malls.

Shopping malls offer various niches for microbial populations, potentially serving as sources and reservoirs for the spread of microorganisms of public health concern. However, knowledge about the microbiome and the distribution of human pathogens in malls is largely unknown. Here, we examine the microbial community dynamics and genotypes of potential pathogens from floor and escalator surfaces in shopping malls and adjacent road dusts and greenbelt soils. The distribution pattern of microbial communities is driven primarily by habitats and seasons. A significant enrichment of human-associated microbiota in the indoor environment indicates that human interactions with surfaces might be another strong driver for mall microbiomes. Neutral community models suggest that the microbial community assembly is strongly driven by stochastic processes. Distinct performances of microbial taxonomic signatures for environmental classifications indicate the consistent differences of microbial communities of different seasons/habitats and the strong anthropogenic effect on homogenizing microbial communities of shopping malls. Indoor environments harbored higher concentrations of human pathogens than outdoor samples, also carrying a high proportion of antimicrobial resistance-associated multidrug efflux genes and virulence genes. These findings enhanced the understanding of the microbiome in the built environment and the interactions between humans and the built environment, providing a basis for tracking biothreats and communicable diseases and developing sophisticated early warning systems. IMPORTANCE Shopping malls are distinct microbial environments which can facilitate a constant transmission of microorganisms of public health concern between humans and the built environment or between human and human. Despite extensive investigation of the natural environmental microbiome, no comprehensive profile of microbial ecology has been reported in malls. Characterizing microbial distribution, potential pathogens, and antimicrobial resistance will enhance our understanding of how these microbial communities are formed, maintained, and transferred and help establish a baseline for biosurveillance of potential public health threats in malls.

Analysis of Factors Influencing Air Quality in Different Periods during COVID-19: A Case Study of Tangshan, China.

This study aimed to analyze the main factors influencing air quality in Tangshan during COVID-19, covering three different periods: the COVID-19 period, the Level I response period, and the Spring Festival period. Comparative analysis and the difference-in-differences (DID) method were used to explore differences in air quality between different stages of the epidemic and different years. During the COVID-19 period, the air quality index (AQI) and the concentrations of six conventional air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3-8h) decreased significantly compared to 2017-2019. For the Level I response period, the reduction in AQI caused by COVID-19 control measures were 29.07%, 31.43%, and 20.04% in February, March, and April of 2020, respectively. During the Spring Festival, the concentrations of the six pollutants were significantly higher than those in 2019 and 2021, which may be related to heavy pollution events caused by unfavorable meteorological conditions and regional transport. As for the further improvement in air quality, it is necessary to take strict measures to prevent and control air pollution while paying attention to meteorological factors.

Immunotoxicity In Vitro Assays for Environmental Pollutants under Paradigm Shift in Toxicity Tests.

With the outbreak of COVID-19, increasingly more attention has been paid to the effects of environmental factors on the immune system of organisms, because environmental pollutants may act in synergy with viruses by affecting the immunity of organisms. The immune system is a developing defense system formed by all metazoans in the course of struggling with various internal and external factors, whose damage may lead to increased susceptibility to pathogens and diseases. Due to a greater vulnerability of the immune system, immunotoxicity has the potential to be the early event of other toxic effects, and should be incorporated into environmental risk assessment. However, compared with other toxicity endpoints, e.g., genotoxicity, endocrine toxicity, or developmental toxicity, there are many challenges for the immunotoxicity test of environmental pollutants; this is due to the lack of detailed mechanisms of action and reliable assay methods. In addition, with the strong appeal for animal-free experiments, there has been a significant shift in the toxicity test paradigm, from traditional animal experiments to high-throughput in vitro assays that rely on cell lines. Therefore, there is an urgent need to build high-though put immunotoxicity test methods to screen massive environmental pollutants. This paper reviews the common methods of immunotoxicity assays, including assays for direct immunotoxicity and skin sensitization. Direct immunotoxicity mainly refers to immunosuppression, for which the assays mostly use mixed immune cells or isolated single cells from animals with obvious problems, such as high cost, complex experimental operation, strong variability and so on. Meanwhile, there have been no stable and standard cell lines targeting immune functions developed for high-throughput tests. Compared with direct immunotoxicity, skin sensitizer screening has developed relatively mature in vitro assay methods based on an adverse outcome pathway (AOP), which points out the way forward for the paradigm shift in toxicity tests. According to the experience of skin sensitizer screening, this paper proposes that we also should seek appropriate nodes and establish more complete AOPs for immunosuppression and other immune-mediated diseases. Then, effective in vitro immunotoxicity assay methods can be developed targeting key events, simultaneously coordinating the studies of the chemical immunotoxicity mechanism, and further promoting the paradigm shift in the immunotoxicity test.

Spatiotemporal variations and the ecological risks of organophosphate esters in Laizhou Bay waters between 2019 and 2021: Implying the impacts of the COVID-19 pandemic.

Organophosphate esters (OPEs) are a group of synthetic chemicals used in numerous consumer products such as plastics and furniture. The Coronavirus Disease 2019 (COVID-19) pandemic significantly slowed anthropogenic activities and reduced the emissions of pollutants. Meanwhile, the mismanagement of large quantities of disposable plastic facemasks intensified the problems of plastic pollution and leachable pollutants in coastal waters. In this study, the joint effects of the COVID-19 outbreak on the occurrence of 12 targeted OPEs in the waters of Laizhou Bay (LZB) were investigated. The results showed that the median total OPE concentrations were 725, 363, and 109 ng L-1 in the sewage treatment plant effluent, river water, and bay water in 2021, decreased significantly (p < 0.05) by 67%, 68%, and 70%, respectively, compared with those before the COVID-19 outbreak. The release potential of targeted OPEs from disposable surgical masks in the LZB area was ∼0.24 kg yr-1, which was insufficient to increase the OPE concentration in the LZB waters. The concentrations of most individual OPEs significantly decreased in LZB waters from 2019 to 2021, except for TBOEP and TNBP. Spatially, a lower concentration of OPEs was found in the Yellow River estuary area in 2021 compared with that before the COVID-19 pandemic due to the high content of suspended particulate matter in the YR. A higher total OPE concentration was observed along the northeastern coast of LZB, mainly owing to the construction of an artificial island since 2020. The ecological risks of the OPE mixture in LZB waters were lower than those before the COVID-19 outbreak. However, TCEP, TNBP, and BDP should receive continuous attention because of their potential ecological risks to aquatic organisms.

CFD Simulations of Ventilation and Interunit Dispersion in Dormitory Complex: A Case Study of Epidemic Outbreak in Shanghai.

Since the beginning of March 2022, a new round of COVID-19 outbreaks in Shanghai has led to a sharp increase in the number of infected people. It is important to identify possible pollutant transmission routes and predict potential infection risks for infectious diseases. Therefore, this study investigated the cross-diffusion of pollutants caused by natural ventilation, including external windows and indoor ventilation windows, under three wind directions in a densely populated building environment with the CFD method. In this study, CFD building models were developed based on an actual dormitory complex and surrounding buildings under realistic wind conditions to reproduce the airflow fields and transmission paths of pollutants. This paper adopted the Wells-Riley model to assess the risk of cross-infection. The biggest risk of infection was when a source room was located on the windward side, and the risk of infection in other rooms on the same side as the source room was large in the windward direction. When pollutants were released from room 8, north wind resulted in the highest concentration of pollutants in room 28, reaching 37.8%. This paper summarizes the transmission risks related to the indoor and outdoor environments of compact buildings.

Effects of air pollution and weather on the initial COVID-19 outbreaks in United States, Italy, Spain, and China: A comparative study.

Contrasting effects have been identified in association of weather (temperature and humidity) and pollutant gases with COVID-19 infection, which could be derived from the influence of lockdowns and season change. The influence of pollutant gases and climate during the initial phases of the pandemic, before the closures and the change of season in the northern hemisphere, is unknown. Here, we used a spatial-temporal Bayesian zero-inflated-Poisson model to test for short-term associations of weather and pollutant gases with the relative risk of COVID-19 disease in China (first outbreak) and the countries with more cases during the initial pandemic (the United States, Spain and Italy), considering also the effects of season and lockdown. We found contrasting association between pollutant gases and COVID-19 risk in the United States, Italy, and Spain, while in China it was negatively associated (except for SO2 ). COVID-19 risk was positively associated with specific humidity in all countries, while temperature presented a negative effect. Our findings showed that short-term associations of air pollutants with COVID-19 infection vary strongly between countries, while generalized effects of temperature (negative) and humidity (positive) with COVID-19 was found. Our results show novel information about the influence of pollution and weather on the initial outbreaks, which contribute to unravel the mechanisms during the beginning of the pandemic.

Advances of microfluidic lung chips for assessing atmospheric pollutants exposure.

Atmospheric pollutants, including particulate matters, nanoparticles, bioaerosols, and some chemicals, have posed serious threats to the environment and the human's health. The lungs are the responsible organs for providing the interface betweenthecirculatory system and the external environment, where pollutant particles can deposit or penetrate into bloodstream circulation. Conventional studies to decipher the mechanismunderlying air pollution and human health are quite limited, due to the lack of reliable models that can reproduce in vivo features of lung tissues after pollutants exposure. In the past decade, advanced near-to-native lung chips, combining cell biology with bioengineered technology, present a new strategy for atmospheric pollutants assessment and narrow the gap between 2D cell culture and in vivo animal models. In this review, the key features of artificial lung chips and the cutting-edge technologies of the lung chip manufacture are introduced. The recent progresses of lung chip technologies for atmospheric pollutants exposure assessment are summarized and highlighted. We further discuss the current challenges and the future opportunities of the development of advanced lung chips and their potential utilities in atmospheric pollutants associated toxicity testing and drug screening.

Mapping the long-term associations between air pollutants and COVID-19 risks and the attributable burdens in the continental United States.

Numerous studies have investigated the associations between COVID-19 risks and long-term exposure to air pollutants, revealing considerable heterogeneity and even contradictory regional results. Studying the spatial heterogeneity of the associations is essential for developing region-specific and cost-effective air-pollutant-related public health policies for the prevention and control of COVID-19. However, few studies have investigated this issue. Using the USA as an example, we constructed single/two-pollutant conditional autoregressions with random coefficients and random intercepts to map the associations between five air pollutants (PM2.5, O3, SO2, NO2, and CO) and two COVID-19 outcomes (incidence and mortality) at the state level. The attributed cases and deaths were then mapped at the county level. This study included 3108 counties from 49 states within the continental USA. The county-level air pollutant concentrations from 2017 to 2019 were used as long-term exposures, and the county-level cumulative COVID-19 cases and deaths through May 13, 2022, were used as outcomes. Results showed that considerably heterogeneous associations and attributable COVID-19 burdens were found in the USA. The COVID-19 outcomes in the western and northeastern states appeared to be unaffected by any of the five pollutants. The east of the USA bore the greatest COVID-19 burdens attributable to air pollution because of its high pollutant concentrations and significantly positive associations. PM2.5 and CO were significantly positively associated with COVID-19 incidence in 49 states on average, whereas NO2 and SO2 were significantly positively associated with COVID-19 mortality. The remaining associations between air pollutants and COVID-19 outcomes were not statistically significant. Our study provided implications regarding where a major concern should be placed on a specific air pollutant for COVID-19 control and prevention, as well as where and how to conduct additional individual-based validation research in a cost-effective manner.

On the transition of major pollutant and O3 production regime during Covid-19 lockdowns.

Lockdowns enforced amid the pandemic facilitated the evaluation of the impact of emission reductions on air quality and the production regime of O3 under NOx reduction. Analysis of space-time variation of various pollutants (PM10, PM2.5, NOx, CO, O3 and VOC or TNMHC) through the lockdown phases at eight typical stations (Urban/Metro, Rural/high vegetation and coastal) is carried out. It reveals how the major pollutant (PM10 or PM2.5 or O3, or CO) differs from station to station as lockdowns progress depending on geography, land-use pattern and efficacy of lockdown implementation. Among the stations analyzed, Delhi (Chandnichowk), the most polluted (PM10 = 203 µgm-3; O3 = 17.4 ppbv) in pre-lockdown, experienced maximum reduction during the first phase of lockdown in PM2.5 (-47%), NO2 (-40%), CO (-37%) while O3 remained almost the same (2% reduction) to pre-lockdown levels. The least polluted Mahabaleshwar (PM10 = 45 µgm-3; O3 = 54 ppbv) witnessed relatively less reduction in PM2.5 (-2.9%), NO2 (-4.7%), CO (-49%) while O3 increased by 36% to pre-lockdown levels. In rural stations with lots of greenery, O3 is the major pollutant attributed to biogenic VOC emissions from vegetation besides lower NO levels. In other stations, PM2.5 or PM10 is the primary pollutant. At Chennai, Jabalpur, Mahabaleshwar and Goa, the deciding factor of Air Quality Index (AQI) remained unchanged, with reduced values. Particulate matter, PM10 decided AQI for three stations (dust as control component), and PM2.5 decided the same for two but within acceptable limits for stations. Improvement of AQI through control of dust would prove beneficial for Chennai and Patiala; anthropogenic emission control would work for Chandani chowk, Goa and Patiala; emission control of CO is required for Mahabaleshwar and Thiruvanathapuram. Under low VOC/NOx ratio conditions, O3 varies with the ratio, NO/NO2, with a negative (positive) slope indicating VOC-sensitive (NOx-sensitive) regime. Peak O3 isopleths as a function of NOx and VOC depicting distinct patterns suggest that O3 variation is entirely non-linear for a given NOx or VOC.

Evaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy (Euro 6).

Euro 6 is the latest vehicle emission standards for pollutants such as CO, NO2 and PM, that all new vehicles must comply, and it was introduced in September 2015 in South Korea. This study examined the effect of Euro 6 by comparing the measured pollutant concentrations after 2016 (Euro 6-era) to the estimated concentrations without Euro 6. The concentration without Euro 6 was estimated by first modeling the air quality using various environmental factors related to diesel vehicles, meteorological conditions, temporal information such as date and precursors in 2002-2015 (pre-Euro 6-era), and then applying the model to predict the concentration after 2016. In this study, we used both recurrent neural network (RNN) and random forest (RF) algorithms to model the air quality and showed that RNN can achieve higher R2 (0.634 ~ 0.759 depending on pollutants) than RF, making it more suitable for air quality modeling. According to our results, the measured concentrations during 2016-2019 were lower than the concentrations predicted using RNN by - 1.2%, - 3.4%, and - 4.8% for CO, NO2 and PM10. Such reduction can be attributed to the result of Euro 6.