[Variations in PM2.5 Composition and Sources During 2020-2021 COVID-19 Epidemic Periods in Nanjing].

To understand the changes in chemical composition and sources of PM2.5 under the extreme reduction background during the COVID-19 epidemic periods in Nanjing, hourly observation results of PM2.5 components (water-soluble inorganic ions, carbonaceous components, and inorganic elements) of two epidemic events from January to March 2020 and June to August 2021 were analyzed. In comparison to that during pre-epidemic periods, the concentration of NO3- during the two epidemic control periods decreased by 52.9% and 43.0%, respectively, which was larger than the decreases in NH4+(46.4% and 31.6%) and SO42-(33.8% and 16.5%). Since the observation site was located close to a main road, the decrease in elemental carbon (EC, 35.4% and 20.6%) was higher than that in organic carbon (OC, 11.1% and 16.2%). In reference to the variations in the characteristic ratios of the bulk components mentioned above, the epidemic control showed a more substantial influence on traffic emissions than industrial activities. The concentration time series of PM2.5 major components over the epidemic periods indicated that NOx from local traffic emissions had substantial contributions to the formation of NO3-, which led to local short-term PM2.5 pollution. In addition, the positive matrix factorization (PMF) model was used to analyze the hourly observation data of PM2.5 components. The seven identified factors were linked with metallurgy, firework and firecracker combustions, road traffic emissions, coal combustion, dust resuspension, secondary sulfate, and secondary nitrate. Because the nitrate was unstable under high temperature, the contribution of secondary nitrate to PM2.5 during the epidemic control period of 2021 (summer, 21.2%) was much lower than that during the epidemic control period of 2020 (winter, 60.6%); however, the formation of secondary components always dominated the contribution of PM2.5 sources. Therefore, emissions of NOx and SO2 should be further controlled to continuously reduce ambient PM2.5 concentrations in Chinese cities.

Prediction and assessment of the impact of COVID-19 lockdown on air quality over Kolkata: a deep transfer learning approach.

The present study focuses on the prediction and assessment of the impact of lockdown because of coronavirus pandemic on the air quality during three different phases, viz., normal periods (1 January 2018-23 March 2020), complete lockdown (24 March 2020-31 May 2020), and partial lockdown (1 June 2020-30 September 2020). We identify the most important air pollutants influencing the air quality of Kolkata during three different periods using Random Forest, a tree-based machine learning (ML) algorithm. It is found that the ambient air quality of Kolkata is mainly affected with the aid of particulate matter or PM (PM10 and PM2.5). However, the effect of the lockdown is most prominent on PM2.5 which spreads in the air of Kolkata due to diesel-driven vehicles, domestic and commercial combustion activities, road dust, and open burning. To predict urban PM2.5 and PM10 concentrations 24 h in advance, we use a deep learning (DL) model, namely, stacked-bidirectional long short-term memory (stacked-BDLSTM). The model is trained during the normal periods, and it shows the superiority over some supervised ML models, like support vector machine, K-nearest neighbor classifier, multilayer perceptron, long short-term memory, and statistical time series forecasting model autoregressive integrated moving average. This pre-trained stacked-BDLSTM is applied to predict the concentrations of PM2.5 and PM10 during the pandemic situation of two cases, viz., complete lockdown and partial lockdown using a deep model-based transfer learning (TL) approach (TLS-BDLSTM). Transfer learning aims to utilize the information gained from one problem to improve the predictive performance of a learning model for a different but related problem. Our work helps to demonstrate how TL is useful when there is a scarcity of data during the COVID-19 pandemic regarding the drastic change in concentration of pollutants. The results reveal the best prediction performance of TLS-BDLSTM with a lead time of 24 h as compared to some well-known traditional ML and statistical models and the pre-trained stacked-BDLSTM. The prediction is then validated using the real-time data obtained during the complete lockdown due to COVID second wave (16 May-15 June 2021) with different time steps, e.g., 24 h, 48 h, 72 h, and 96-120 h. TLS-BDLSTM involving transfer learning is seen to outperform the said comparing methods in modeling the long-term temporal dependency of multivariate time series data and boost the forecast efficiency not only in single step, but also in multiple steps. The proposed methodologies are effective, consistent, and can be used by operational organizations to utilize in monitoring and management of air quality.

[Impacts of Emission and Meteorological Conditions on Air Pollutants at Various Sites Around the COVID-19 Lockdown in Wuhan].

The random forest algorithm was used to separate the mass concentrations of six air pollutants (SO2, NO2, CO, PM10, PM2.5, and O3) contributed by emissions and meteorological conditions. Their variations for five types of sites including Wuhan's central urban, suburb, industrial, the third ring road traffic, and urban background sites were investigated. The results showed that the values of PM2.5/CO, PM10/CO, and NO2/CO during the lockdown period decreased by 10.8-21.7, 9.34-24.7, and 14.4-22.1 times compared with the period before the lockdown, indicating that the contributions of emissions to PM2.5, PM10, and NO2 were reduced. O3/CO increased by 50.1-61.5 times, implying that the secondary formation increased obviously. The contributions of emissions to various types of pollutants all increased after the lockdown. During the lockdown period, affected by the operation of some uninterrupted industrial processes, PM2.5 concentrations in industrial areas dropped the least (20.5%). Compared with the lockdown period, residential activities, transportation, and industrial production were basically restored after the lockdown, resulting in the alleviation of the reduction in PM2.5 emission-related concentrations. The increase in emission-related O3 concentrations could be associated with the decreased NO and PM2.5 concentrations during the lockdown period. The elevated O3 partially offset the improved air quality brought by the reduced NO2and PM2.5 concentrations. After the lockdown, ρ(O3) related with meteorology at the suburban and urban background sites increased by 16.2 µg·m-3 and 16.1 µg·m-3, respectively, which could be attributed to the increased ambient temperature and decreased relative humidity. The decrease in PM2.5 and increase in O3 concentrations caused by reduced traffic and industrial emissions at the third ring road traffic and central urban regions can provide reference for the current coordinated and precise control of PM2.5 and O3 in subregions.

Seasonal and Spatial Variations of the Oxidative Properties of Ambient PM2.5 in the Po Valley, Italy, before and during COVID-19 Lockdown Restrictions.

This study describes the chemical and toxicological characteristics of fine particulate matter (PM2.5) in the Po Valley, one of the largest and most polluted areas in Europe. The investigated samples were collected in the metropolitan area of Milan during the epidemic lockdown and their toxicity was evaluated by the oxidative potential (OP), measured using ascorbic acid (OPAA) and dithiothreitol (OPDTT) acellular assays. The study was also extended to PM2.5 samples collected at different sites in the Po Valley in 2019, to represent the baseline conditions in the area. Univariate correlations were applied to the whole dataset to link the OP responses with the concentrations of the major chemical markers of vehicular and biomass burning emissions. Of the two assays, OPAA was found mainly sensitive towards transition metals released from vehicular traffic, while OPDTT towards the PM carbonaceous components. The impact of the controlling lockdown restrictions on PM2.5 oxidative properties was estimated by comparing the OP values in corresponding time spans in 2020 and 2019. We found that during the full lockdown the OPAA values decreased to 80-86% with respect to the OP data in other urban sites in the area, while the OPDTT values remained nearly constant.

Suspended Particulate Matter Analysis of Pre and During Covid Lockdown Using Google Earth Engine Cloud Computing: A Case Study of Ukai Reservoir.

Presence of suspended particulate matter (SPM) in a waterbody or a river can be caused by multiple parameters such as other pollutants by the discharge of poorly maintained sewage, siltation, sedimentation, flood and even bacteria. In this study, remote sensing techniques were used to understand the effects of pandemic-induced lockdown on the SPM concentration in the lower Tapi reservoir or Ukai reservoir. The estimation was done using Landsat-8 OLI (Operational Land Imager) having radiometric resolution (12-bit) and a spatial resolution of 30 m. The Google Earth Engine (GEE) cloud computing platform was used in this study to generate the products. The GEE is a semi-automated workflow system using a robust approach designed for scientific analysis and visualization of geospatial datasets. An algorithm was deployed, and a time-series (2013-2020) analysis was done for the study area. It was found that the average mean value of SPM in Tapi River during 2020 is lowest than the last seven years at the same time.

Impacts of Wildfire Smoke and Air Pollution on a Pediatric Population with Asthma: A Population-Based Study.

Wildfires are increasing yearly in number and severity as a part of the evolving climate crisis. These fires are a significant source of air pollution, a common driver of flares in cardiorespiratory disease, including asthma, which is the most common chronic disease of childhood. Poorly controlled asthma leads to significant societal costs through morbidity, mortality, lost school and work time and healthcare utilization. This retrospective cohort study set in Calgary, Canada evaluates the relationship between asthma exacerbations during wildfire smoke events and equivalent low-pollution periods in a pediatric asthma population. Air pollution was based on daily average levels of PM2.5. Wildfire smoke events were determined by combining information from provincial databases and local monitors. Exposures were assumed using postal codes in the health record at the time of emergency department visits. Provincial claims data identified 27,501 asthma exacerbations in 57,375 children with asthma between 2010 to 2021. Wildfire smoke days demonstrated an increase in asthma exacerbations over the baseline (incidence rate ratio: 1.13; 95% CI: 1.02-1.24); this was not seen with air pollution in general. Increased rates of asthma exacerbations were also noted yearly in September. Asthma exacerbations were significantly decreased during periods of COVID-19 healthcare precautions.

The Relationship between the Transmission of Different SARS-CoV-2 Strains and Air Quality: A Case Study in China.

Coronavirus Disease 2019 (COVID-19) has been a global public health concern for almost three years, and the transmission characteristics vary among different virus variants. Previous studies have investigated the relationship between air pollutants and COVID-19 infection caused by the original strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, it is unclear whether individuals might be more susceptible to COVID-19 due to exposure to air pollutants, with the SARS-CoV-2 mutating faster and faster. This study aimed to explore the relationship between air pollutants and COVID-19 infection caused by three major SARS-CoV-2 strains (the original strain, Delta variant, and Omicron variant) in China. A generalized additive model was applied to investigate the associations of COVID-19 infection with six air pollutants (PM2.5, PM10, SO2, CO, NO2, and O3). A positive correlation might be indicated between air pollutants (PM2.5, PM10, and NO2) and confirmed cases of COVID-19 caused by different SARS-CoV-2 strains. It also suggested that the mutant variants appear to be more closely associated with air pollutants than the original strain. This study could provide valuable insight into control strategies that limit the concentration of air pollutants at lower levels and would better control the spread of COVID-19 even as the virus continues to mutate.

[Numerical Response Analysis of PM2.5-O3 Compound Pollution in Beijing].

The multi-scale variation trend of PM2.5-O3 compound pollution events was analyzed based on air quality data, meteorological data, and COVID-19 data in Beijing from 2015 to 2020. For the threshold of compound pollution, a compound pollution index was proposed, and the numerical response trend was evaluated based on the generalized additive model. A distributed lag nonlinear model was introduced to analyze the risk response relationship between compound pollution and influencing factors. The results showed that the events of PM2.5-O3 compound pollution in Beijing decreased annually. At the same time, due to the influence of pollutant emissions and meteorological conditions, there were obvious seasonal effects, week effects, holiday effects, and epidemic effects. The composite pollution index had no correlation with rainfall but had a linear positive correlation with O3 and air temperature and a nonlinear correlation with other explanatory variables. Air pollutants and meteorological conditions had obvious lag effects on the composite pollution index, and the lag effects were mainly concentrated in 1-3 d. PM2.5, PM10, O3, SO2, and air temperature in high-value areas significantly increased the risk of compound pollution. The CO (1-6 mg·m-3), NO2 (38-118 µg·m-3), and relative humidity (54%-87%) in the median section would also increase the risk of compound pollution, as would low wind speed. The compound pollution events showed a trend of multi-day continuous pollution in the numerical response. Compared with PM2.5 and PM10, compound pollution events were more dependent on O3, and the compound pollution rate in high-value areas was 30.7%-47.5%. CO and relative humidity had little effect on compound pollution events. The air temperature had the greatest impact, and 84.7% of the composite pollution incidents occurred at 20-30℃.

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.

Investigation of the Exposure of Schoolchildren to Ultrafine Particles (PM0.1) during the COVID-19 Pandemic in a Medium-Sized City in Indonesia.

The health risk of schoolchildren who were exposed to airborne fine and ultrafine particles (PM0.1) during the COVID-19 pandemic in the Jambi City (a medium-sized city in Sumatra Island), Indonesia was examined. A questionnaire survey was used to collect information on schoolchildren from selected schools and involved information on personal profiles; living conditions; daily activities and health status. Size-segregated ambient particulate matter (PM) in school environments was collected over a period of 24 h on weekdays and the weekend. The personal exposure of PM of eight selected schoolchildren from five schools was evaluated for a 12-h period during the daytime using a personal air sampler for PM0.1 particles. The schoolchildren spent their time mostly indoors (~88%), while the remaining ~12% was spent in traveling and outdoor activities. The average exposure level was 1.5~7.6 times higher than the outdoor level and it was particularly high for the PM0.1 fraction (4.8~7.6 times). Cooking was shown to be a key parameter that explains such a large increase in the exposure level. The PM0.1 had the largest total respiratory deposition doses (RDDs), particularly during light exercise. The high level of PM0.1 exposure by indoor sources potentially associated with health risks was shown to be important.

Impact of the SARS-CoV-2 lockdown measures in Southern Spain on PM10 trace element and gaseous pollutant concentrations.

Trace element concentrations within PM10, gaseous pollutants (NO2 and SO2), and PM10 levels were studied during the Covid-19 lockdown at a regional level in Southern Spain (Andalusia). Pollutant concentrations were compared considering different mobility periods (pre-lockdown, lockdown, and relaxation) in 2020 and previous years (2013-2016). An acute decrease in NO2 levels (<50%) was observed as a consequence of traffic diminution during the confinement period. Moreover, a lower reduction in PM10 levels and a non-clear pattern for SO2 levels were observed. During the lockdown period, PM10 elements released from traffic emissions (Sn and Sb) showed the highest concentration diminution in the study area. Regarding the primary industrial sites, there were no significant differences in V, Ni, La, and Cr concentration reduction during 2020 associated with industrial activity (stainless steel and oil refinery) in Algeciras Bay. Similarly, concentrations of Zn showed the same behaviour at Cordoba, indicating that the Zn-smelter activity was not affected by the lockdown. Nevertheless, stronger reductions of Cu, Zn, and As in Huelva during the confinement period indicated a decrease in the nearby Cu-smelter emissions. Brick factories in Bailen were also influenced by the confinement measures, as corroborated by the marked decrease in concentrations of Ni, V, Cu, and Zn during the lockdown compared to that from previous years. This work has shown the baseline concentrations of trace elements of PM10, which is of great value to air quality managers in order to minimise pollution levels by applying the confinement of the population, affecting both traffic and industrial anthropogenic activities.

The association of airborne particulate matter and benzo[a]pyrene with the clinical course of COVID-19 in patients hospitalized in Poland.

Air pollution can adversely affect the immune response and increase the severity of the viral disease. The present study aimed to explore the relationship between symptomatology, clinical course, and inflammation markers of adult patients with coronavirus disease 2019 (COVID-19) hospitalized in Poland (n = 4432) and air pollution levels, i.e., mean 24 h and max 24 h level of benzo(a)pyrene (B(a)P) and particulate matter <10 µm (PM10) and <2.5 µm (PM2.5) during a week before their hospitalization. Exposures to PM2.5 and B(a)P exceeding the limits were associated with higher odds of early respiratory symptoms of COVID-19 and hyperinflammatory state: interleukin-6 > 100 pg/mL, procalcitonin >0.25 ng/mL, and white blood cells count >11 × 103/mL. Except for the mean 24 h PM10 level, the exceedance of other air pollution parameters was associated with increased odds for oxygen saturation <90%. Exposure to elevated PM2.5 and B(a)P levels increased the odds of oxygen therapy and death. This study evidences that worse air quality is related to increased severity of COVID-19 and worse outcome in hospitalized patients. Mitigating air pollution shall be an integral part of measures undertaken to decrease the disease burden during a pandemic of viral respiratory illness.

Molecular characteristics of ambient organic aerosols in Shanghai winter before and after the COVID-19 outbreak.

During the global pandemic of COVID-19, the world adopted different strategies to avoid the human and economic loss, and so does China. The reduction of human activities during this time period caused reduction in PM emissions. This study adopted a HPLC-Q-TOF-MS to compare the chemical compositions of ambient aerosol samples collected in Shanghai winter before (2018, 2019) and after (2021) the COVID-19 outbreak. The identified compositions were classified into subgroups of CHO, CHN, CHON, CHONS, CHOS and CHN compounds. Results showed that CHO compounds and CHON compounds were dominating the organic compounds in ESI- and ESI+, respectively. The average percentages of CHO- compounds were 57.97 % in 2018, 58.98 % in 2019, and 43.93 % in 2021, respectively. The average percentages of CHON+ compounds were 52.74 % in 2018, 50.34 % in 2019, and 52.02 % in 2021, respectively. The proportion of aliphatic compounds increased gradually during the three years, especially in 2021, indicating that CHO compounds were less affected by aromatic precursors after the COVID-19 outbreak. The contribution of anthropogenic emissions in Shanghai was weakened compared with the previous years. In addition, there was an enhanced emission source containing hydroxyl for CHOS compound formation in 2021. The variations of atmospheric oxidation degree among the three years were not significant.

A review on characteristics and mitigation strategies of indoor air quality in underground subway stations.

Due to the rapidly increasing ridership and the relatively enclosed underground space, the indoor air quality (IAQ) in underground subway stations (USSs) has attracted more public attention. The air pollutants in USSs, such as particulate matter (PM), CO2 and volatile organic compounds (VOCs), are hazardous to the health of passengers and staves. Firstly, this paper presents a systematic review on the characteristics and sources of air pollutants in USSs. According to the review work, the concentrations of PM, CO2, VOCs, bacteria and fungi in USSs are 1.1-13.2 times higher than the permissible concentration limits specified by WHO, ASHRAE and US EPA. The PM and VOCs are mainly derived from the internal and outdoor sources. CO2 concentrations are highly correlated with the passenger density and the ventilation rate while the exposure levels of bacteria and fungi depend on the thermal conditions and the settled dust. Then, the online monitoring, fault detection and prediction methods of IAQ are summarized and the advantages and disadvantages of these methods are also discussed. In addition, the available control strategies for improving IAQ in USSs are reviewed, and these strategies are classified and compared from different viewpoints. Lastly, challenges of the IAQ management in the context of the COVID-19 epidemic and several suggestions for underground stations' IAQ management in the future are put forward. This paper is expected to provide a comprehensive guidance for further research and design of the effective prevention measures on air pollutants in USSs so as to achieve more sustainable and healthy underground environment.

Impact of air pollutants on influenza-like illness outpatient visits under COVID-19 pandemic in the subcenter of Beijing, China.

This study aimed to explore the association between air pollutants and outpatient visits for influenza-like illnesses (ILI) under the coronavirus disease 2019 (COVID-19) stage in the subcenter of Beijing. The data on ILI in the subcenter of Beijing from January 1, 2018 to December 31, 2020 were obtained from the Beijing Influenza Surveillance Network. A generalized additive Poisson model was applied to examine the associations between the concentrations of air pollutants and daily outpatient visits for ILI when controlling meteorological factors and temporal trend. A total of 171 943 ILI patients were included. In the pre-coronavirus disease 2019 (COVID-19) stage, an increased risk of ILI outpatient visits was associated to a high air quality index (AQI) and the high concentrations of particulate matter less than 2.5 (PM2.5 ), particulate matter 10 (PM10 ), sulphur dioxide (SO2 ), nitrogen dioxide (NO2 ), and carbon monoxide (CO), and a low concentration of ozone (O3 ) on lag0 day and lag1 day, while a higher increased risk of ILI outpatient visits was observed by the air pollutants in the COVID-19 stage on lag0 day. Except for PM10 , the concentrations of other air pollutants on lag1 day were not significantly associated with an increased risk of ILI outpatient visits during the COVID-19 stage. The findings that air pollutants had enhanced immediate effects and diminished lag-effects on the risk of ILI outpatient visits during the COVID-19 pandemic, which is important for the development of public health and environmental governance strategies.

The Short- and Long-Run Impacts of Air Pollution on Human Health: New Evidence from China.

Under the background of the far-reaching impact of the COVID-19 epidemic on global economic development, the interactive effect of economic recovery and pollution rebound makes the research topic of air pollution and human health receive attention again. Matching a series of new datasets and employing thermal inversion as an instrumental variable, this study investigates the physical and mental health effect of air pollution jointly in China. We find that in the short run, the above inference holds for both physical and mental health. These short-run influences are credible after a series of robustness checks and vary with different individual characteristics and geographical locations. We also find that in the long run, air pollution only damages mental health. Finally, this study calculates the health cost of air pollution. The above findings indicate that in China, the effect of air pollution on physical and mental health cannot be ignored. The government needs to consider the heterogeneity and long-run and short-run differences in the health effects of air pollution when formulating corresponding environmental and medical policies.

Public health in Global South: effect of environmental pollutant PM2.5 on the incidence and mortality of SARS-CoV-2 in Karachi, Lahore, and Islamabad.

OBJECTIVE: Environmental pollution has undoubtedly been established as a planetary, intergenerational, and existential threat to global human health and safety. Environmental pollution is adversely affecting the world, mainly the countries where human health is not a priority aspect, and this has been exacerbated due to the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), and pandemic is known as "COVID pandemic". This study investigates the association of environmental pollutants, particulate matter (PM2.5), with SARS-CoV-2 daily cases and deaths in Karachi, Lahore, and Islamabad, Pakistan, presenting the perspectives from the Global South. MATERIALS AND METHODS: The day-to-day PM2.5 levels were recorded from the metrological website, Real-Time Air Quality Index-AQI. The corresponding data on the COVID cases and deaths in Karachi, Lahore, and Islamabad were obtained from August 1, 2020, to September 30, 2021, from the Health Ministry and National Command Operations Centre Pakistan. RESULTS: The mean values for PM2.5 in Karachi were 110.4±46.2; in Lahore 174.0±83.2; and in Islamabad 107.1±40.0. The COVID-19 mean daily cases in Karachi were 538.9±446.6; Lahore 398.3±403.1; and Islamabad 212.2±187.6; and mean daily deaths in Karachi were 9.2±8.3; Lahore 9.3±9.7; and Islamabad 1.8±1.8. The results further depicted that the SARS-CoV-2 cases were 2.86 times higher in Karachi and 1.4 times higher in Lahore than in Islamabad. Similarly, the SARS-CoV-2 deaths were 3.6 and 2.8 times higher in Karachi and Lahore, respectively, compared to Islamabad. CONCLUSIONS: The findings claim that cases and deaths augmented significantly along with PM2.5 levels. These empirical estimates demonstrate an association between PM2.5 and SARS-CoV-2 daily cases and deaths in the cities of the Global South. These findings can contribute to policy-making decisions about addressing air pollutants and climate concerns in developing countries and create an urgency to develop a strategy for minimizing environmental pollution. This study can also steer the actions needed to address the environmental problems in developing countries to improve public health and safety.

Substantial changes in Gaseous pollutants and health effects during COVID-19 in Delhi, India.

Background: Coronavirus disease has affected the entire population worldwide in terms of physical and environmental consequences. Therefore, the current study demonstrates the changes in the concentration of gaseous pollutants and their health effects during the COVID-19 pandemic in Delhi, the national capital city of India. Methodology: In the present study, secondary data on gaseous pollutants such as nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), ammonia (NH3), and ozone (O3) were collected from the Central Pollution Control Board (CPCB) on a daily basis. Data were collected from January 1, 2020, to September 30, 2020, to determine the relative changes (%) in gaseous pollutants for pre-lockdown, lockdown, and unlockdown stages of COVID-19. Results: The current findings for gaseous pollutants reveal that concentration declined in the range of 51%-83% (NO), 40%-69% (NOx), 31%-60% (NO2), and 25%-40% (NH3) during the lockdown compared to pre-lockdown period, respectively. The drastic decrease in gaseous pollutants was observed due to restricted measures during lockdown periods. The level of ozone was observed to be higher during the lockdown periods as compared to the pre-lockdown period. These gaseous pollutants are linked between the health risk assessment and hazard identification for non-carcinogenic. However, in infants (0-1 yr), Health Quotient (HQ) for daily and annual groups was found to be higher than the rest of the exposed group (toddlers, children, and adults) in all the periods. Conclusion: The air quality values for pre-lockdown were calculated to be "poor category to "very poor" category in all zones of Delhi, whereas, during the lockdown period, the air quality levels for all zones were calculated as "satisfactory," except for Northeast Delhi, which displayed the "moderate" category. The computed HQ for daily chronic exposure for each pollutant across the child and adult groups was more than 1 (HQ > 1), which indicated a high probability to induce adverse health outcomes.

Short-term effects of ambient particulate matter (PM1, PM2.5 and PM10) on influenza-like illness in Guangzhou, China.

BACKGROUND: Particulate matter (PM) has been linked to respiratory infections in a growing body of evidence. Studies on the relationship between ILI (influenza-like illness) and PM1 (particulate matter with aerodynamic diameter ≤1 µm) are, however, scarce. The purpose of this study was to investigate the effects of PM on ILI in Guangzhou, China. METHODS: Daily ILI cases, air pollution records (PM1, PM2.5, PM10 and gaseous pollutants), and metrological data between 2014 and 2019 were gathered from Guangzhou, China. To estimate the risk of ILI linked with exposure to PM pollutants, a quasi-Poisson regression was used. Additionally, subgroup analyses stratified by gender, age and season were carried out. RESULTS: For each 10 µg/m3 increase of PM1 and PM2.5 over the past two days (lag01), and PM10 over the past three days (lag02), the relative risks (RR) of ILI were 1.079 (95% confidence interval [CI]: 1.050, 1.109), 1.044 (95% CI: 1.027, 1.062) and 1.046 (95% CI: 1.032, 1.059), respectively. The estimated risks for men and women were substantially similar. The effects of PM pollutants between male and female were basically equivalent. People aged 15-24 years old were more susceptive to PM pollutants. CONCLUSIONS: It implies that PM1, PM2.5 and PM10 are all risk factors for ILI, the health impacts of PM pollutants vary by particle size. Reducing the concentration of PM1 needs to be considered when generating a strategy to prevent ILI.

Spatial shifting of COVID-19 clusters and disease association with environmental parameters in India: A time series analysis.

BACKGROUND: The viability and virulence of COVID-19 are complex in nature. Although the relationship between environmental parameters and COVID-19 is well studied across the globe, in India, such studies are limited. This research aims to explore long-term exposure to weather conditions and the role of air pollution on the infection spread and mortality due to COVID-19 in India. METHOD: District-level COVID-19 data from April 26, 2020 to July 10, 2021 was used for the study. Environmental determinants such as land surface temperature, relative humidity (RH), Sulphur dioxide (SO2), Nitrogen dioxide (NO2), Ozone (O3), and Aerosol Optical Depth (AOD) were considered for analysis. The bivariate spatial association was used to explore the spatial relationship between Case Fatality Rate (CFR) and these environmental factors. Further, the Bayesian multivariate linear regression model was applied to observe the association between environmental factors and the CFR of COVID-19. RESULTS: Spatial shifting of COVID-19 cases from Western to Southern and then Eastern parts of India were well observed. The infection rate was highly concentrated in most of the Western and Southern regions of India, while the CFR shows more concentration in Northern India along with Maharashtra. Four main spatial clusters of infection were recognized during the study period. The time-series analysis indicates significantly more CFR with higher AOD, O3, and NO2 in India. CONCLUSIONS: COVID-19 is highly associated with environmental parameters and air pollution in India. The study provides evidence to warrant consideration of environmental parameters in health models to mediate potential solutions. Cleaner air is a must to mitigate COVID-19.