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.

Cities of Central Asia: New hotspots of air pollution in the world

Central Asian cities are one of the hotspots for air pollution worldwide. There are limited studies and knowledge regarding air quality variation in this region. This study investigated PM2.5 temporal variations and the influence of meteorological parameters on PM2.5 concentrations for six major cities in Central Asia: Almaty and Astana (Kazakhstan), Ashgabat (Turkmenistan), Bishkek (Kyrgyzstan), Dushanbe (Tajikistan), and Tashkent (Uzbekistan). The results show severe air quality deterioration in the cities with annual PM2.5 concentrations up to ten-fold higher than the limits. A clear seasonal pattern with winter peaks was observed in Almaty, Bishkek, and Astana, whereas winter and summer were highly polluted in Tashkent and Dushanbe. Based on the pollution profiles, cities were classified into several clusters. Episodes with high PM2.5 concentrations were evaluated for regional pollutant transportation using the HYSPLIT model. The results of this investigation highlight a significant discrepancy in official emissions inventory studies. While previous studies have suggested that transportation is the primary source of air pollution, the approach to estimate the share of emission sources was based on an outdated methodology that obscures information on the most hazardous pollutants, including PM2.5. This study shows that coal combustion is the primary source of PM2.5 pollution in most cities, offering policymakers critical insights into the sources of air pollution in the region. These findings demonstrate the need for policymakers to take swift action to address coal use and adopt effective measures to mitigate PM2.5 pollution, thereby improving the health and well-being of the population.

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.

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.

Meteorological-Data-Based Modeling for PV Performance Optimization

Developing a sustainable and reliable photovoltaic (PV) energy system requires a comprehensive analysis of solar profiles and an accurate prediction of solar energy performance at the study site. Installing the PV modules with optimal tilt and azimuth angles has a significant impact on the total irradiance delivered to the PV modules. This paper proposes a comprehensive optimization model to integrate total irradiance models with the PV temperature model to find the optimal year-round installation parameters of PV modules. A novel integration between installation parameters and the annual average solar energy is presented, to produce the maximum energy output. The results suggest an increase in energy yields of 4% compared to the conventional scheme, where tilt angle is equal to the latitude and the PV modules are facing south. This paper uses a real-time dataset for the NEOM region in Saudi Arabia to validate the superiority of the proposed model compared to the conventional scheme, but it can be implemented as a scheme wherever real-time data are available.

Random forest regression on joint role of meteorological variables, demographic factors, and policy response measures in COVID-19 daily cases: global analysis in different climate zones.

Different sources of factors in environment can affect the spread of COVID-19 by influencing the diffusion of the virus transmission, but the collective influence of which has hardly been considered. This study aimed to utilize a machine learning algorithm to assess the joint effects of meteorological variables, demographic factors, and government response measures on COVID-19 daily cases globally at city level. Random forest regression models showed that population density was the most crucial determinant for COVID-19 transmission, followed by meteorological variables and response measures. Ultraviolet radiation and temperature dominated meteorological factors, but the associations with daily cases varied across different climate zones. Policy response measures have lag effect in containing the epidemic development, and the pandemic was more effectively contained with stricter response measures implemented, but the generalized measures might not be applicable to all climate conditions. This study explored the roles of demographic factors, meteorological variables, and policy response measures in the transmission of COVID-19, and provided evidence for policymakers that the design of appropriate policies for prevention and preparedness of future pandemics should be based on local climate conditions, population characteristics, and social activity characteristics. Future work should focus on discerning the interactions between numerous factors affecting COVID-19 transmission.

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.

Characteristics and sources of carbonaceous aerosols in a semi-arid city: Quantifying anthropogenic and meteorological impacts.

Carbonaceous aerosols have great adverse impacts on air quality, human health, and climate. However, there is a limited understanding of carbonaceous aerosols in semi-arid areas. The correlation between carbonaceous aerosols and control measures is still unclear owing to the insufficient information regarding meteorological contribution. To reveal the complex relationship between control measures and carbonaceous aerosols, offline and online observations of carbonaceous aerosols were conducted from October 8, 2019 to October 7, 2020 in Hohhot, a semi-arid city. The characteristics and sources of carbonaceous aerosols and impacts of anthropogenic emissions and meteorological conditions were studied. The annual mean concentrations (± standard deviation) of fine particulate matter (PM2.5), organic carbon (OC), and elemental carbon (EC) were 42.81 (±40.13), 7.57 (±6.43), and 2.25 (±1.39) µg m-3, respectively. The highest PM2.5 and carbonaceous aerosol concentrations were observed in winter, whereas the lowest was observed in summer. The result indicated that coal combustion for heating had a critical role in air quality degradation in Hohhot. A boost regression tree model was applied to quantify the impacts of anthropogenic emissions and meteorological conditions on carbonaceous aerosols. The results suggested that the anthropogenic contributions of PM2.5, OC, and EC during the COVID-19 lockdown period were 53.0, 15.0, and 2.36 µg m-3, respectively, while the meteorological contributions were 5.38, 2.49, and -0.62 µg m-3, respectively. Secondary formation caused by unfavorable meteorological conditions offset the emission reduction during the COVID-19 lockdown period. Coal combustion (46.4% for OC and 35.4% for EC) and vehicular emissions (32.0% for OC and 50.4% for EC) were the predominant contributors of carbonaceous aerosols. The result indicated that Hohhot must regulate coal use and vehicle emissions to reduce carbonaceous aerosol pollution. This study provides new insights and a comprehensive understanding of the complex relationships between control strategies, meteorological conditions, and air quality.

Response of PM2.5 pollution to meteorological and anthropogenic emissions changes during COVID-19 lockdown in Hunan Province based on WRF-Chem model.

In December 2019, the New Crown Pneumonia (the COVID-19) outbroke around the globe, and China imposed a nationwide lockdown starting as early as January 23, 2020. This decision has significantly impacted China's air quality, especially the sharp decrease in PM2.5 (aerodynamic equivalent diameter of particulate matter less than or equal to 2.5 µm) pollution. Hunan Province is located in the central and eastern part of China, with a "horseshoe basin" topography. The reduction rate of PM2.5 concentrations in Hunan province during the COVID-19 (24.8%) was significantly higher than the national average (20.3%). Through the analysis of the changing character and pollution sources of haze pollution events in Hunan Province, more scientific countermeasures can be provided for the government. We use the Weather Research and Forecasting with Chemistry (WRF-Chem, V4.0) model to predict and simulate the PM2.5 concentrations under seven scenarios before the lockdown (2020.1.1-2020.1.22) and during the lockdown (2020.1.23-2020.2.14). Then, the PM2.5 concentrations under different conditions is compared to differentiate the contribution of meteorological conditions and local human activities to PM2.5 pollution. The results indicate the most important cause of PM2.5 pollution reduction is anthropogenic emissions from the residential sector, followed by the industrial sector, while the influence of meteorological factors contribute only 0.5% to PM2.5. The explanation is that emission reductions from the residential sector contribute the most to the reduction of seven primary contaminants. Finally, we trace the source and transport path of the air mass in Hunan Province through the Concentration Weight Trajectory Analysis (CWT). We found that the external input of PM2.5 in Hunan Province is mainly from the air mass transported from the northeast, accounting for 28.6%-30.0%. To improve future air quality, there is an urgent need to burn clean energy, improve the industrial structure, rationalize energy use, and strengthen cross-regional air pollution synergy control.

Research progress on influence of environmental and meteorological parameters on transmission of coronavirus disease 2019

Novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) is spreading rapidly around the world and has become a global pandemic. Meteorological factors have been recognized as one of the critical factors that influence the epidemiology and transmission of infectious diseases. In this context, the World Meteorological Organization and scholars at home and abroad have paid extensive attention to the relationships of environment and meteorology with COVID-19. This paper systematically collected and sorted out relevant domestic and foreign studies, and reviewed the latest research progress on the impact of environmental and meteorological factors on COVID-19, classifying them into typical meteorological factors (such as temperature, humidity, and wind speed), local environmental factors (such as indoor enclosed environment, ventilation, disinfection, and air conditioning), and air pollution. Current research evidence suggests that typical meteorological factors, local environmental factors, and air pollutants are closely related to the transmission of COVID-19. However, the results of different studies are still divergent due to uncertainty about the influencing mechanism, and differences in research areas and methods. This review elucidated the importance of environmental and meteorological factors to the spread of COVID-19, and provided useful implications for the control of further large-scale transmission of COVID-19 and the development of prevention and control strategies under different environmental and meteorological conditions.Copyright © 2022, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.

The impact of meteorological factors on the spread of COVID-19.

Background: Clinical studies suggest that warmer climates slow the spread of viral infections. In addition, exposure to cold weakens human immunity. Aim: This study describes the relationship between meteorological indicators, the number of cases, and mortality in patients with confirmed coronavirus disease 2019 (COVID-19). Patients and Methods: This was a retrospective observational study. Adult patients who presented to the emergency department with confirmed COVID-19 were included in the study. Meteorological data [mean temperature, minimum (min) temperature, maximum (max) temperature, relative humidity, and wind speed] for the city of Istanbul were collected from the Istanbul Meteorology 1st Regional Directorate. Results: The study population consisted of 169,058 patients. The highest number of patients were admitted in December (n = 21,610) and the highest number of deaths (n = 46) occurred in November. In a correlation analysis, a statistically significant, negative correlation was found between the number of COVID-19 patients and mean temperature (rho = -0.734, P < 0.001), max temperature (rho = -0.696, P < 0.001) or min temperature (rho = -0.748, P < 0.001). Besides, the total number of patients correlated significantly and positively with the mean relative humidity (rho = 0.399 and P = 0.012). The correlation analysis also showed a significant negative relationship between the mean, maximum, and min temperatures and the number of deaths and mortality. Conclusion: Our results indicate an increased number of COVID-19 cases during the 39-week study period when the mean, max, and min temperatures were consistently low and the mean relative humidity was consistently high.

Influence of meteorological factors on the severity of COVID-19 in Spain: Observational ecological study. / Influencia de los factores meteorológicos en la gravedad de la COVID-19 en España: estudio observacional ecológico.

OBJECTIVES: Evaluating whether meteorological and geographical variables could be associated with the severity of COVID-19 in Spain. METHODS: An ecological study was performed to analyze the influence of meteorological and geographical factors in hospital admissions and deaths due to COVID-19 in the 52 provinces of Spain (24 coastal and 28 inland regions), during the first three pandemic waves. Medical and mortality data were collected from the CarlosIII Health Institute (ISCIII) and meteorological variables were requested to the Spanish State Meteorological Agency (AEMET). RESULTS: Regarding the diagnosed cases it is remarkable that the percentage of patients hospitalized for COVID-19 was lower in the coastal provinces than in the inland ones (8.7±2.6% vs. 11.5±2.6%; P=9.9×10-5). Furthermore, coastal regions registered a lower percentage of mortality than inland regions (2.0±0.6% vs. 3.1±0.8%; P=1.7×10-5). Mean air temperature was inversely correlated both with COVID-19 hospitalizations (Rho: -0.59; P=3.0×10-6) and mortality (Rho: -0.70; P=5.3×10-9). In those provinces with a mean air temperature <10°C mortality by COVID-19 was twice that of those with >16°C. Finally, we found an association between mortality and the location of the province (coastal/inland), altitude, patient age and the average air temperature; the latter was inversely and independently correlated with mortality (non-standardized ß coeff.: -0.24; 95%CI: -0.31 to -0.16; P=2.38×10-8). CONCLUSIONS: The average air temperature was inversely associated with COVID-19 mortality in our country during the first three waves of the pandemic.

Two years of volatile organic compound online in situ measurements at the Site Instrumental de Recherche par Télédétection Atmosphérique (Paris region, France) using proton-transfer-reaction mass spectrometry

Volatile organic compounds (VOCs) have direct influences on air quality and climate. They indeed play a key role in atmospheric chemistry as precursors of secondary pollutants, such as ozone (O3) and secondary organic aerosols (SOA). In this respect, long-term datasets of in situ atmospheric measurements are crucial for characterizing the variability of atmospheric chemical composition, its sources, and trends. The ongoing establishment of the Aerosols, Cloud, and Trace gases Research InfraStructure (ACTRIS) allows implementation of the collection and provision of such high-quality datasets. In this context, online and continuous measurements of O3, nitrogen oxides (NOx), and aerosols have been carried out since 2012 at the SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique) observatory, located in the Paris region, France. Within the last decade, VOC measurements were conducted offline at SIRTA, until the implementation of real-time monitoring which started in January 2020 using a proton-transfer-reaction quadrupole mass spectrometer (PTR-Q-MS).The dataset acquired during the first 2 years of online VOC measurements provides insights into their seasonal and diurnal variabilities. The additional long-term datasets obtained from co-located measurements (NOx, aerosol physical and chemical properties, meteorological parameters) are used to better characterize the atmospheric conditions and to further interpret the obtained results. Results also include insights into VOC main sources and the influence of meteorological conditions and air mass origin on their levels in the Paris region. Due to the COVID-19 pandemic, the year 2020 notably saw a quasi-total lockdown in France in spring and a lighter one in autumn. Therefore, the focus is placed on the impact of these lockdowns on the VOC variability and sources. A change in the behaviour of VOC markers for anthropogenic sources was observed during the first lockdown, reflecting a change in human activities. A comparison with gas chromatography data from the Paris city centre consolidates the regional representativity of the SIRTA station for benzene, while differences are observed for shorter-lived compounds with a notable impact of their local sources. This dataset could be further used as input for atmospheric models and can be found at 10.14768/f8c46735-e6c3-45e2-8f6f-26c6d67c4723 (Simon et al., 2022a).

Influence of Meteorological Factors on Covid-19 Incidence in the Conditions of Ukrain

Objective: The coronavirus disease (COVID-19) is a problem for the health care systems of many countries around the world. Seasonal nature of influenza and other the respiratory viral diseases is commonly known. The nature of the relationship between the frequency of registration of cases of COVID-19 and natural factors is still being studied by researchers. The purpose is to determine the influence of air temperature, relative humidity, wind speed, and atmospheric pressure on the incidence of the coronavirus disease COVID-19 in the conditions of Ukraine. Materials and methods. Official reports of the Ministry of Health of Ukraine and data from daily monitoring of meteorological indicators conducted by the Sumy Regional Hydrometeorology Center were used in the paper. Descriptive and analytical ways of epidemiological method of investigation were applied. The search for parameters of interrelation between the frequency of registration of COVID-19 cases and meteorological cases took place using of program "Statistica", namely the relevant tools of this program: "Analysis"/ "Multiple regression". Results and Discussion: In the period under study from March 25, 2020 to December 31, 2021 in Sumy Oblast of Ukraine, three waves of rise in the incidence were registered. In the third wave of rise in the incidence, in autumn 2021 the frequency of registration of COVID-19 cases reached 1684.9 per 100 thousand of people, despite the fact that almost 70 % of the population had already recovered or were vaccinated. Meteorological factors in the conditions of Ukraine have little influence on the rate of spread of COVID-19. The value of multiple correlation coefficients was within those limits, which are considered moderate in terms of influence. A moderate inverse correlation was established between the frequency of registration of COVID-19 cases and indicators of air temperature, and a direct correlations-with indicators of relative air humidity. Conclusion(s): In the conditions of Ukraine, the studied meteorological factors (air temperature, relative humidity, wind speed, atmospheric pressure) indirectly influenced the intensity of the epidemic process of COVID-19. the strength of this influence was either weak or moderate.Copyright © 2023, Ibn Sina Trust. All rights reserved.

Deep neural network for monitoring the growth of COVID-19 epidemic using meteorological covariates

Growth of an epidemic is influenced by the natural variation in climatic conditions and enforcement variation in government stringency policies. Though these variations do not prompt an instant change in the growth of an epidemic, effects of climatic conditions and stringency policies become apparent over time. Time-lagged relationships and functional dynamic connectivity among meteorological covariates and stringency levels generate many lagged features deemed to be important for prediction of reproduction rate, a measure for growth of an epidemic. This empirical study examines the importance scores of lagged features and implements distributed lag inspired feature selection with back testing for model selection and forecasting. A verification forecasting scheme is developed for continuous monitoring of the growth of an epidemic. We have demonstrated the monitoring process by computing a week ahead expected target of the reproduction rate and then by computing a one day ahead verification forecast to evaluate the progress towards the expected target. This evaluation procedure will aid the analysts with a decision making tool for any early adjustment of control options to suppress the transmission.

An effect of temperature on the spread of COVID-19 in Qatar, Kuwait and other gulf countries. (Special issue on COVID.)

Background: COVID-19 has emerged as a serious pandemic that emerged during since the end of 2019. The dissemination and survival of coronaviruses have been demonstrated to be affected by ambient temperature in epidemiological and laboratory research. The goal of this investigation was to see if temperature plays a role in the infection produced by this novel coronavirus. Methods: Between March 29, 2020, and September 29, 2020, daily confirmed cases and meteoro-logical parameters in many Gulf countries were collected. Using a generalized additive model, we investigated the nonlinear relationship between mean temperature and COVID-19 confirmed cases.. To further investigate the association, we employed a piecewise linear regression. Results: According to the exposure-response curves, the association between mean temperature and COVID-19 cases was nearly linear in the window of 21 - 30C while it is almost flat beyond that window. When the number was below 21C (lag 0-14), each 1C increase was associated with a 4.861 percent (95 percent CI: 3.209 - 6.513) increase in mean temperature (lag 0-14). Our sensitiv-ity analysis confirmed these conclusions. Conclusions: Our findings show a positive linear association between mean temperature and the number of COVID-19 cases with a threshold of 21C. There is little evidence that COVID-19 case numbers would rise as the weather becomes colder, which has important consequences for making health strategy and decision.

The Effect of Meteorological Factors on the COVID-19 Pandemic in Northeast Turkiye.

Introduction Although various studies have been conducted on the relationship between meteorological factors and coronavirus disease 2019 (COVID-19), this issue has not been sufficiently clarified. In particular, there are a limited number of studies on the course of COVID-19 in the warmer-humidity seasons. Methods Patients presenting to the emergency departments of health institutions and to clinics set aside for cases of suspected COVID-19 in the province of Rize between 1 June and 31 August 2021 and who met the case definition based on the Turkish COVID-19 epidemiological guideline were included in this retrospective study. The effect of meteorological factors on case numbers throughout the study was investigated. Results During the study period, 80,490 tests were performed on patients presenting to emergency departments and clinics dedicated to patients with suspected COVID-19. The total case number was 16,270, with a median daily number of 64 (range 43-328). The total number of deaths was 103, with a median daily figure of 1.00 (range 0.00-1.25). According to the Poisson distribution analysis, it is found that the number of cases tended to increase at temperatures between 20.8 and 27.2°C. Conclusion It is predicted that the number of COVID-19 cases will not decrease with the increase in temperature in temperate regions with high rainfall. Therefore, unlike influenza, there may not be seasonal variation in the prevalence of COVID-19. The requisite measures should be adopted in health systems and hospitals to manage increases in case numbers associated with changes in meteorological factors.

Effects of COVID‐19 lockdown measures on nitrogen dioxide and black carbon concentrations close to a major Italian motorway

During the first half of 2020, the Italian government imposed several restrictions to limit the spread of the COVID‐19 pandemic: at the beginning of March, a heavy lockdown regime was introduced leading to a drastic reduction of traffic and, consequently, traffic‐related emissions. The aim of this study is to evaluate the effects of these restrictions on pollutant concentrations close to a stretch of the Italian A22 motorway lying in the Alpine Adige valley. In particular, the analysis focuses on measured concentrations of nitrogen dioxide (NO2) and black carbon (BC). Results show that, close to the motorway, NO2 concentrations dropped by around 45% during the lockdown period with respect to the same time period of the previous 3 years. The equivalent analysis for BC shows that the component related to biomass burning, mostly due to domestic heating, was not particularly affected by the restrictions, while the BC component related to fossil fuels, directly connected to traffic, plummeted by almost 60% with respect to the previous years. Since atmospheric concentrations of pollutants depend both on emissions and meteorological conditions, which can mask the variations in the emission regime, a random forest algorithm is also applied to the measured concentrations, in order to better evaluate the effects of the restrictions on emissions. This procedure allows for obtaining business‐as‐usual and meteorologically normalized time series of both NO2 and BC concentrations. The results derived from the random forest algorithm clearly confirm the drop in NO2 emissions at the beginning of the lockdown period, followed by a slow and partial recovery in the following months. They also confirm that, during the lockdown, emissions of the BC component due to biomass burning were not significantly affected, while those of the BC component related to fossil fuels underwent an abrupt drop.

Improving Short-Term QPF Using Geostationary Satellite All-Sky Infrared Radiances: Real-Time Ensemble Data Assimilation and Forecast during the PRECIP 2020 and 2021 Experiments

The Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) aims to improve our understanding of extreme rainfall processes in the East Asian summer monsoon. A convection-permitting ensemble-based data assimilation and forecast system (the PSU WRF-EnKF system) was run in real time in the summers of 2020–21 in advance of the 2022 field campaign, assimilating all-sky infrared (IR) radiances from the geostationary Himawari-8 and GOES-16 satellites, and providing 48-h ensemble forecasts every day for weather briefings and discussions. This is the first time that all-sky IR data assimilation has been performed in a real-time forecast system at a convection-permitting resolution for several seasons. Compared with retrospective forecasts that exclude all-sky IR radiances, rainfall predictions are statistically significantly improved out to at least 4–6 h for the real-time forecasts, which is comparable to the time scale of improvements gained from assimilating observations from the dense ground-based Doppler weather radars. The assimilation of all-sky IR radiances also reduced the forecast errors of large-scale environments and helped to maintain a more reasonable ensemble spread compared with the counterpart experiments that did not assimilate all-sky IR radiances. The results indicate strong potential for improving routine short-term quantitative precipitation forecasts using these high-spatiotemporal-resolution satellite observations in the future.Significance StatementDuring the summers of 2020/21, the PSU WRF-EnKF data assimilation and forecast system was run in real time in advance of the 2022 Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP), assimilating all-sky (clear-sky and cloudy) infrared radiances from geostationary satellites into a numerical weather prediction model and providing ensemble forecasts. This study presents the first-of-its-kind systematic evaluation of the impacts of assimilating all-sky infrared radiances on short-term qualitative precipitation forecasts using multiyear, multiregion, real-time ensemble forecasts. Results suggest that rainfall forecasts are improved out to at least 4–6 h with the assimilation of all-sky infrared radiances, comparable to the influence of assimilating radar observations, with benefits in forecasting large-scale environments and representing atmospheric uncertainties as well.

Does environmental quality and weather induce COVID-19: Case study of Istanbul, Turkey

The coronavirus pandemic has infected more than 100 million people worldwide with COVID-19, with millions of deaths across the globe. In this research, we explored the effects of environmental and weather variables with daily COVID-19 cases and COVID-19 fatalities in Istanbul, Turkey. Turkey has the 8th highest number of COVID-19 cases globally, with the highest infections and deaths in Istanbul. This may be the first study to conduct a comprehensive investigation for environmental quality (air quality pollutants, e.g., PM2.5 and PM10, ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide, etc.), weather parameters (temperature, humidity) and COVID-19 in Turkey. The authors collected meteorological data from 11 March 2020 to 8 February 2021 and COVID-19 data from Istanbul and other regions. The results from empirical estimations, correlation analysis, and quantile on quantile techniques support that air quality and temperature significantly influence COVID-19 deaths in Istanbul. This research may help policymakers and health scientists to take specific measures to reduce the spread of coronavirus across different global cities.The effects of air quality on COVID-19 in Istanbul was investigated.The study applied correlation and quantile on quantile techniques over daily data.Temperature significantly induces the spread of COVID-19 in Istanbul at all quantiles.Air quality and Nitrogen are positively linked with COVID-19 new cases.