Variations in land surface temperatures in small-scale urban areas in Vietnam during Covid-19 restrictions: Case studies from Da Nang, Hue and Vinh City.

Land surface temperature (LST) is an important variable in urban microclimate research. At the end of 2019, the emergence of Covid-19 pandemic has changed the world in a manner that forced many countries to impose restrictions in human activities. As a measure to prevent the expansion of Covid-19 infections, most of the major cities have entered a prolonged lockdown period and reduction in human activities between the early 2020 and the late 2021. These restrictions were strict in most of the cities in Southeast Asia, particularly in Vietnam. The present study investigated the variations in LST and NDVI observed in three rapidly growing urban areas, namely Da Nang, Hue and Vinh, in Vietnam using Landsat-8 imagery acquired between 2017 and 2022. There has been a slight reduction in LST in the study sites, particularly in Da Nang City, during the lockdown period but not as high as observed in recently conducted studies from big metropolitan cities, including in Vietnam. It is also observed that LST estimated from built-up areas and other impervious surfaces remained relatively stable during the study period which is similar to the results from recent studies.

Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning.

Social distancing measures and shelter-in-place orders to limit mobility and transportation were among the strategic measures taken to control the rapid spreading of COVID-19. In major metropolitan areas, there was an estimated decrease of 50 to 90 percent in transit use. The secondary effect of the COVID-19 lockdown was expected to improve air quality, leading to a decrease in respiratory diseases. The present study examines the impact of mobility on air quality during the COVID-19 lockdown in the state of Mississippi (MS), USA. The study region is selected because of its non-metropolitan and non-industrial settings. Concentrations of air pollutants-particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), ozone (O3), nitrogen oxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO)-were collected from the Environmental Protection Agency, USA from 2011 to 2020. Because of limitations in the data availability, the air quality data of Jackson, MS were assumed to be representative of the entire region of the state. Weather data (temperature, humidity, pressure, precipitation, wind speed, and wind direction) were collected from the National Oceanic and Atmospheric Administration, USA. Traffic-related data (transit) were taken from Google for the year 2020. The statistical and machine learning tools of R Studio were used on the data to study the changes in air quality, if any, during the lockdown period. Weather-normalized machine learning modeling simulating business-as-scenario (BAU) predicted a significant difference in the means of the observed and predicted values for NO2, O3, and CO (p < 0.05). Due to the lockdown, the mean concentrations decreased for NO2 and CO by -4.1 ppb and -0.088 ppm, respectively, while it increased for O3 by 0.002 ppm. The observed and predicted air quality results agree with the observed decrease in transit by -50.5% as a percentage change of the baseline, and the observed decrease in the prevalence rate of asthma in MS during the lockdown. This study demonstrates the validity and use of simple, easy, and versatile analytical tools to assist policymakers with estimating changes in air quality in situations of a pandemic or natural hazards, and to take measures for mitigating if the deterioration of air quality is detected.

Modeling fuel consumption and emissions by taxis in Tabriz, Iran: uncertainty and sensitivity analyses.

Taxis pose a higher threat to global climate change and human health through air emissions. However, the evidence on this topic is scarce, especially, in developing countries. Therefore, this study conducted estimation of fuel consumption (FC) and emission inventories on Tabriz taxi fleet (TTF), Iran. A structured questionnaire to obtain operational data of TTF, municipality organizations, and literature review were used as data sources. Then modeling was used to estimate fuel consumption ratio (FCR), emission factors (EFs), annual FC, and emissions of TTF using uncertainty analysis. Also, the impact of COVID-19 pandemic period was considered on the studied parameters. The results showed that TTF have high FCRs of 18.68 L/100 km (95% CI=17.67-19.69 L/100 km), which are not affected by age or mileage of taxis, significantly. The estimated EFs for TTF are higher than Euro standards, but the differences are not significant. However, it is critical as can be an indication of inefficiency of periodic regulatory technical inspection tests for TTF. COVID-19 pandemic caused significant decrease in annual total FC and emissions (9.03-15.6%), but significant increase in EFs of per-passenger-kilometer traveled (47.9-57.3%). Annual vehicle-kilometer-traveled by TTF and the estimated EFs for gasoline-compressed natural gas bi-fueled TTF are the main influential parameters in the variability of annual FC and emission levels. More studies on sustainable FC and emissions mitigation strategies are needed for TTF.

The effect of research on COVID-19 and PM2.5 on the localization of humanitarian aid.

The spatiotemporal variation of the death and tested positive cases is poorly understood during the respiratory coronavirus disease 2019 (COVID-19) pandemic. On the other hand, COVID-19's spread was not significantly slowed by pandemic maps. The aim of this study is to investigate the connection between COVID-19 distribution and airborne PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm). Long-term exposure to high levels of PM2.5 is significantly connected to respiratory diseases in addition to being a potential carrier of viruses. Between April 2020 and March 2021, data on COVID-19-related cases were gathered for all prefectures in Japan. There were 9159, 109,078, and 451,913 cases of COVID-19 that resulted in death, severe illness, and positive tests, respectively. Additionally, we gathered information on PM2.5 from 1119 air quality monitoring stations that were deployed across the 47 prefectures. By using the statistical analysis tools in the Geographical Information System (GIS) software, it was found that the residents of prefectures with high PM2.5 concentrations were the most susceptible to COVID-19. Additionally, the World Health Organization-Air Quality Guidelines (WHO-AQG) relative risk (RR) of 1.04 (95% CI: 1.01-1.08), which was used to compute the PM2.5-caused deaths, was employed as well. Approximately 1716 (95% CI: 429-3,432) cases of PM2.5-related deaths were thought to have occurred throughout the study period. Despite the possibility that the actual numbers of both COVID19 and PM2.5-caused deaths are higher, humanitarian actors could use PM2.5 data to localize the efforts to minimize the spread of COVID-19.

Impact of COVID-19 lockdown on water quality in China during 2020 and 2022: two case surges.

The COVID-19 severely affected the world in 2020. Taking the two outbreaks in China in 2020 and 2022 as examples, the spatiotemporal changes in surface water quality levels and CODMn and NH3-N concentrations were analyzed, and the relationships between the variations in the two pollutants and environmental and social factors were evaluated. The results showed that during the two lockdowns, due to the total water consumption (including industrial, agricultural, and domestic water) decreased, the proportion of good water quality increased by 6.22% and 4.58%, and the proportion of polluted water decreased by 6.00% and 3.98%, the quality of water environment has been improved significantly. However, the proportion of excellent water quality decreased by 6.19% after entering the unlocking period. Before the second lockdown period, the average CODMn concentration exhibited a "falling, rising, and falling" trend, while the average NH3-N concentration changed in the opposite direction. The correlation analysis revealed that the increasing trend of pollutant concentrations was positively correlated with longitude and latitude, and weakly correlated with DEM and precipitation. A slight decrease trend in NH3-N concentration was negatively correlated with the population density variation and positively correlated with the temperature variation. The relationship between the change in the number of confirmed cases in provincial regions and the change in pollutant concentrations was uncertain, with positive and negative correlations. This study demonstrates the impact of lockdowns on water quality and the possibility of improving water quality through artificial regulation, which can provide a reference basis for water environmental management.

[Changes in Secondary Inorganic Ions in PM2.5 at Different Pollution Stages Before and After COVID-19 Control].

To investigate the change characteristics of secondary inorganic ions in PM2.5 at different pollution stages before and after COVID-19, the online monitoring of winter meteorological and atmospheric pollutant concentrations in Zhengzhou from December 15, 2019 to February 15, 2020 was conducted using a high-resolution (1 h) online instrument. This study analyzed the causes of the haze process of COVID-19, the diurnal variation characteristics of air pollutants, and the distribution characteristics of air pollutants at different stages of haze.The results showed that Zhengzhou was mainly controlled by the high-pressure ridge during the haze process, and the weather situation was stable, which was conducive to the accumulation of air pollutants. SNA was the main component of water-soluble ions, accounting for more than 90%. Home isolation measures during COVID-19 had different impacts on the distribution characteristics of air pollutants in different haze stages. After COVID-19, the concentration of PM2.5 in the clean, occurrence, and dissipation stages increased compared with that before COVID-19 but significantly decreased in the development stage. The home isolation policy significantly reduced the high value of PM2.5. The concentrations of NO2, SO2, NH3, and CO were the highest in the haze development stage, showing a trend of first increasing and then decreasing. The concentration of O3 was lowest in the pre-COVID-19 development stage but highest in the post-COVID-19 development stage. The linear correlation between[NH4+]/[SO42-] and[NO3-]/[SO42-] at different time periods before and after COVID-19 was strong, indicating that the home isolation policy of COVID-19 did not change the generation mode of NO3-, and the corresponding reaction was always the main generation mode of NO3-. The correlation between[excess-NH4+] and[NO3-] was high in different periods before COVID-19, and NO3- generation was related to the increase in NH3 or NH4+ in the process of PM2.5 pollution in Zhengzhou.

Evaluation and uncertainty analysis of Himawari-8 hourly aerosol product version 3.1 and its influence on surface solar radiation before and during the COVID-19 outbreak.

The hourly Himawari-8 version 3.1 (V31) aerosol product has been released and incorporates an updated Level 2 algorithm that uses forecast data as an a priori estimate. However, there has not been a thorough evaluation of V31 data across a full-disk scan, and V31 has yet to be applied in the analysis of its influence on surface solar radiation (SSR). This study firstly investigates the accuracy of V31 aerosol products, which includes three categories of aerosol optical depth (AOD) (AODMean, AODPure, and AODMerged) as well as the corresponding Ångström exponent (AE), using ground-based measurements from the AERONET and SKYNET. Results indicate that V31 AOD products are more consistent with ground-based measurements compared to previous products (V30). The highest correlation and lowest error were seen in the AODMerged, with a correlation coefficient of 0.8335 and minimal root mean square error of 0.1919. In contrast, the AEMerged shows a larger discrepancy with measurements unlike the AEMean and AEPure. Error analysis reveals that V31 AODMerged has generally stable accuracy across various ground types and geometrical observation angles, however, there are higher uncertainties in areas with high aerosol loading, particularly for fine aerosols. The temporal analysis shows that V31 AODMerged performs better compared to V30, particularly in the afternoon. Finally, the impacts of aerosols on SSR based on the V31 AODMerged are investigated through the development of a sophisticated SSR estimation algorithm in the clear sky. Results demonstrate that the estimated SSR is significant consistency with those of well-known CERES products, with preservation of 20 times higher spatial resolution. The spatial analysis reveals a significant reduction of AOD in the North China Plain before and during the COVID-19 outbreak, resulting in an average 24.57 W m-2 variation of the surface shortwave radiative forcing in clear sky daytime.

Machine learning model to predict vehicle electrification impacts on urban air quality and related human health effects.

Air pollution is a prevailing environmental problem in cities worldwide. The future vehicle electrification (VE), which in Europe will be importantly fostered by the ban of thermal engines from 2035, is expected to have an important effect on urban air quality. Machine learning models represent an optimal tool for predicting changes in air pollutants concentrations in the context of future VE. For the city of Valencia (Spain), a XGBoost (eXtreme Gradient Boosting package) model was used in combination with SHAP (SHapley Additive exPlanations) analysis, both to investigate the importance of different factors explaining air pollution concentrations and predicting the effect of different levels of VE. The model was trained with 5 years of data including the COVID-19 lockdown period in 2020, in which mobility was strongly reduced resulting in unprecedent changes in air pollution concentrations. The interannual meteorological variability of 10 years was also considered in the analyses. For a 70% VE, the model predicted: 1) improvements in nitrogen dioxide pollution (-34% to -55% change in annual mean concentrations, for the different air quality stations), 2) a very limited effect on particulate matter concentrations (-1 to -4% change in annual means of PM2.5 and PM10), 3) heterogeneous responses in ground-level ozone concentrations (-2% to +12% change in the annual means of the daily maximum 8-h average concentrations). Even at a high VE increase of 70%, the 2021 World Health Organization Air Quality Guidelines will be exceeded for all pollutants in some stations. VE has a potentially important impact in terms of reducing NO2-associated premature mortality, but complementary strategies for reducing traffic and controlling all different air pollution sources should also be implemented to protect human health.

Influence of COVID-19 lockdown on river water quality and assessment of environmental health in an industrialized belt of southern Western Ghats, India.

The COVID-19 pandemic and sudden lockdown have severely hampered the country's economic growth and socio-cultural activities while imparting a positive effect on the overall fitness of the environment especially air and water resources. Increased urbanization and rapid industrialization have led to rising pollution and deterioration of rivers and associated sectors such as agriculture, domestic and commercial needs. However, various available studies in different parts of the country indicate that the COVID-19 pandemic has changed the entire ecosystem. But it is noted that studies are lacking in the southern Western Ghats region of India. Therefore, the present study attempts to investigate how the continuous lockdowns affect the River Water Quality (RWQ) during lockdown (October 2020) and post-lockdown (January 2021) periods in the lower catchments (Eloor-Edayar industrialized belt) of Periyar river, Kerala state, South India. A total of thirty samples (15 samples each) were analyzed based on drinking water quality, irrigational suitability, and multivariate statistical methods to evaluate the physical and chemical status of RWQ. The results of the Water Quality Index (WQI) for assessing the drinking water suitability showed a total of 93% of samples in the excellent and good category during the lockdown, while only 47% of samples were found fit for drinking during the post-lockdown period. Irrigational suitability indices like Mg hazard, KR, PI, SAR, and Wilcox diagram revealed lockdown period samples as more suitable for irrigational activities compared to post-lockdown samples with site-specific changes. Spearman rank correlation analysis indicated EC and TDS with a strong positive correlation to Ca2+, Mg2+, Na+, K+, TH, SO42-, and Cl- during both periods as well as strong positive correlations within the alkaline earth elements (Ca2+ and Mg 2+) and alkalis (Na+ and K+). Three significant components were extracted from principal component analysis (PCA), explaining 88.89% and 96.03% of the total variance for lockdown and post-lockdown periods, respectively. Variables like DO, BOD, Ca2+, NO3-, and Cl- remained in the same component loading during both periods elucidating their natural origin in the basin. The results of health risk assessment based on US EPA represented hazard quotient and hazard index values below the acceptable limit signifying no potential noncarcinogenic risk via oral exposure except As, suggesting children as more vulnerable to the negative effects than adults. Furthermore, this study also shows rejuvenation of river health during lockdown offers ample scope to policymakers, administrators and environmentalists for deriving appropriate plans for the restoration of river health from anthropogenic stress.

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.

An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak.

With the Chinese government revising ambient air quality standards and strengthening the monitoring and management of pollutants such as PM2.5, the concentrations of air pollutants in China have gradually decreased in recent years. Meanwhile, the strong control measures taken by the Chinese government in the face of COVID-19 in 2020 have an extremely profound impact on the reduction of pollutants in China. Therefore, investigations of pollutant concentration changes in China before and after COVID-19 outbreak are very necessary and concerning, but the number of monitoring stations is very limited, making it difficult to conduct a high spatial density investigation. In this study, we construct a modern deep learning model based on multi-source data, which includes remotely sensed AOD data products, other reanalysis element data, and ground monitoring station data. Combining satellite remote sensing techniques, we finally realize a high spital density PM2.5 concentration change investigation method, and analyze the seasonal and annual, the spatial and temporal characteristics of PM2.5 concentrations in Mid-Eastern China from 2016 to 2021 and the impact of epidemic closure and control measures on regional and provincial PM2.5 concentrations. We find that PM2.5 concentrations in Mid-Eastern China during these years is mainly characterized by "north-south superiority and central inferiority", seasonal differences are evident, with the highest in winter, the second highest in autumn and the lowest in summer, and a gradual decrease in overall concentration during the year. According to our experimental results, the annual average PM2.5 concentration decreases by 3.07 % in 2020, and decreases by 24.53 % during the shutdown period, which is probably caused by China's epidemic control measures. At the same time, some provinces with a large share of secondary industry see PM2.5 concentrations drop by more than 30 %. By 2021, PM2.5 concentrations rebound slightly, rising by 10 % in most provinces.

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.

Coordination of chemical analyses under the European Human Biomonitoring Initiative (HBM4EU): Concepts, procedures and lessons learnt.

The European Human Biomonitoring Initiative (HBM4EU) ran from 2017 to 2022 with the aim of advancing and harmonizing human biomonitoring in Europe. More than 40,000 analyses were performed on human samples in different human biomonitoring studies in HBM4EU, addressing the chemical exposure of the general population, temporal developments, occupational exposure and a public health intervention on mercury in populations with high fish consumption. The analyses covered 15 priority groups of organic chemicals and metals and were carried out by a network of laboratories meeting the requirements of a comprehensive quality assurance and control system. The coordination of the chemical analyses included establishing contacts between sample owners and qualified laboratories and monitoring the progress of the chemical analyses during the analytical phase, also addressing status and consequences of Covid-19 measures. Other challenges were related to the novelty and complexity of HBM4EU, including administrative and financial matters and implementation of standardized procedures. Many individual contacts were necessary in the initial phase of HBM4EU. However, there is a potential to develop more streamlined and standardized communication and coordination in the analytical phase of a consolidated European HBM programme.

Size-classified monitoring of ATP bioluminescence for rapid assessment of biological distribution in airborne particulates.

The COVID-19 pandemic ignited massive research into the rapid detection of bioaerosols. In particular, nanotechnology-based detection strategies are proposed as alternatives because of issues in bioaerosol enrichment and lead time for molecular diagnostics; however, the practical implementation of such techniques is still unclear due to obstacles regarding the large research and development effort and investment for the validation. The use of adenosine triphosphate (ATP) bioluminescence (expressed as relative luminescence unit (RLU) per unit volume of air) of airborne particulate matter (PM) to determine the bacterial population as a representative of the total bioaerosols (viruses, bacteria, and fungi) has been raised frequently because of the high reponse speed, resolution, and compatibility with culture-based bioaerosol monitoring. On the other hand, additional engineering attempts are required to confer significance because of the size-classified (bioluminescence for different PM sizes) and specific (bioluminescence per unit PM mass) biological risks of air for providing proper interventions in the case of airborne transmission. In this study, disc-type impactors to cut-off aerosols larger than 1 µm, 2.5 µm, and 10 µm were designed and constructed to collect PM1, PM2.5, and PM10 on sampling swabs. This engineering enabled reliable size-classified bioluminescence signals using a commercial ATP luminometer after just 5 min of air intake. The simultaneous operations of a six-stage Andersen impactor and optical PM spectrometers were conducted to determine the correlations between the resulting RLU and colony forming unit (CFU; from the Andersen impactor) or PM mass concentration (deriving specific bioluminescence).

An empirical and modelling approach to the evaluation of cruise ships' influence on air quality: The case of La Paz, Mexico.

Maritime activity has diverse environmental consequences impacts in port areas, especially for air quality, and the post-COVID-19 cruise tourism market's potential to recover and grow is causing new environmental concerns in expanding port cities. This research proposes an empirical and modelling approach for the evaluation of cruise ships' influence on air quality concerning NO2 and SO2 in the city of La Paz (Mexico) using indirect measurements. EPA emission factors and the AERMOD modelling system coupled to WRF were used to model dispersions, while street-level mobile monitoring data of air quality from two days of 2018 were used and processed using a radial base function interpolator. The local differential Moran's Index was estimated at the intersection level using both datasets and a co-location clustering analysis was performed to address spatial constancy and to identify the pollution levels. The modelled results showed that cruise ships' impact on air quality had maximum values of 13.66 µg/m3 for NO2 and 15.71 µg/m3 for SO2, while background concentrations of 8.80 for NOx and 0.05 for SOx (µg/m3) were found by analysing the LISA index values for intersections not influenced by port pollution. This paper brings insights to the use of hybrid methodologies as an approach to studying the influence of multiple-source pollutants on air quality in contexts totally devoid of environmental data.

Snapshots of wintertime urban aerosol characteristics: Local sources emphasized in ultrafine particle number and lung deposited surface area.

Urban air fine particles are a major health-relating problem. However, it is not well understood how the health-relevant features of fine particles should be monitored. Limitations of PM2.5 (mass concentration of sub 2.5 µm particles), which is commonly used in the health effect estimations, have been recognized and, e.g., World Health Organization (WHO) has released good practice statements for particle number (PN) and black carbon (BC) concentrations (2021). In this study, a characterization of urban wintertime aerosol was done in three environments: a detached housing area with residential wood combustion, traffic-influenced streets in a city centre and near an airport. The particle characteristics varied significantly between the locations, resulting different average particle sizes causing lung deposited surface area (LDSA). Near the airport, departing planes had a major contribution on PN, and most particles were smaller than 10 nm, similarly as in the city centre. The high hourly mean PN (>20 000 1/cm3) stated in the WHO's good practices was clearly exceeded near the airport and in the city centre, even though traffic rates were reduced due to a SARS-CoV-2-related partial lockdown. In the residential area, wood combustion increased both BC and PM2.5, but also PN of sub 10 and 23 nm particles. The high concentrations of sub 10 nm particles in all the locations show the importance of the chosen lower size limit of PN measurement, e.g., WHO states that the lower limit should be 10 nm or smaller. Furthermore, due to ultrafine particle emissions, LDSA per unit PM2.5 was 1.4 and 2.4 times higher near the airport than in the city centre and the residential area, respectively, indicating that health effects of PM2.5 depend on urban environment as well as conditions, and emphasizing the importance of PN monitoring in terms of health effects related to local pollution sources.

Spatiotemporal characteristics and socioeconomic factors of PM2.5 heterogeneity in mainland China during the COVID-19 epidemic.

Spatiotemporal variation of PM2.5 in 2018 and 2020 were compared to analyze the impacts of COVID-19, the spatial heterogeneity of PM2.5, and meteorological and socioeconomic impacts of PM2.5 concentrations heterogeneity in China in 2020 were investigated. The results showed that the annual average PM2.5 concentration in 2020 was 32.73 µg/m3 existing a U-shaped variation pattern, which has decreased by 6.38 µg/m3 compared to 2018. A consistent temporal pattern was found in 2018 and 2020 with significant high values in winter and low in summer. PM2.5 declined dramatically in eastern and central China, where are densely populated and economically developed areas during the COVID-19 epidemic compared with previous years, indicating that the significantly decline of social activities had an important effect on the reduction of PM2.5 concentrations. The lowest PM2.5 was found in August because that precipitation had a certain dilution effect on pollutants. January was the most polluted due to centralized coal burning for heating in North China. Overall, the PM2.5 concentrations in China were spatially agglomerated. The highly polluted contiguous zones were mainly located in northwest China and the central plains city group, while the coastal area and Inner Mongolia were areas with good air quality. Negative correlations were found between natural factors (temperature, precipitation, wind speed and relative humidity) and PM2.5 concentrations, with precipitation has the greatest impact on PM2.5, which are beneficial for reducing PM2.5 concentrations. Among the socio-economic factors, proportion of the secondary industry, number of taxis, per capita GDP, population, and industrial nitrogen oxide emissions have positive correlation effects on PM2.5, while the overall social electricity consumption, industrial sulfur dioxide emissions, green coverage in built-up areas, and total gas and liquefied gas supply have negative correlation effects on the PM2.5.

COVID-19 personal protective equipment (PPE) contamination in coastal areas of Granada, Spain.

The use of disposable personal protective equipment (PPE) as a control measure to avoid transmission against COVID-19 has generated a challenge to the waste management and enhances plastic pollution in the environment. The research aims to monitor the presence of PPE waste and other plastic debris, in a time interval where the use of face mask at specific places was still mandatory, on the coastal areas of Granada (Spain) which belongs to the Mediterranean Sea. Four beaches called La Rijana, La Charca, La Rábita and Calahonda were examined during different periods. The total amount of sampled waste was 17,558 plastic units. The abundance, characteristics and distribution of PPE and other plastic debris were determined. Results showed that the observed amount of total plastic debris were between 2.531·10-2 and 24.487·10-2 units per square meter, and up to 0.136·10-2 for PPE debris, where face masks represented the 92.22 % of the total PPE debris, being these results comparable to previous studies in other coastal areas in the world. On the other hand, total plastic debris densities were in the range from 2.457·10-2 to 92.219·10-2 g/m2 and densities were up to 0.732·10-2 for PPE debris. PPE debris supposed 0.79 % of the weight of total waste and the 0.51 % of total items. Concerning non-PPE plastic waste: cigarettes filters, food containers and styrofoam were the most abundant items (42.95, 10.19 and 16.37 % of total items, respectively). During vacation periods, total plastic debris amount increased 92.19 % compared to non-vacation periods. Regarding type of beaches, the presence of plastic debris was significantly higher on touristic/recreational than in fishing beaches. Data showed no significant differences between accessible and no-accessible beaches, but between periods with restrictive policy about mask face use and periods with non-restrictive policy data suggest significant differences between densities (g/m2) for PPE litter. The amount of PPEs debris is also correlated with the number of cigarettes filters (Person's r = 0.650), food containers (r = 0.782) and other debris (r = 0.63). Finally, although interesting results were provided in this study, further research is required to better understand the consequences of this type of pollution and to provide viable solutions to this problem.

Assessing lake water quality during COVID-19 era using geospatial techniques and artificial neural network model.

The present study evaluates the impact of the COVID-19 lockdown on the water quality of a tropical lake (East Kolkata Wetland or EKW, India) along with seasonal change using Landsat 8 and 9 images of the Google Earth Engine (GEE) cloud computing platform. The research focuses on detecting, monitoring, and predicting water quality in the EKW region using eight parameters-normalized suspended material index (NSMI), suspended particular matter (SPM), total phosphorus (TP), electrical conductivity (EC), chlorophyll-α, floating algae index (FAI), turbidity, Secchi disk depth (SDD), and two water quality indices such as Carlson tropic state index (CTSI) and entropy­weighted water quality index (EWQI). The results demonstrate that SPM, turbidity, EC, TP, and SDD improved while the FAI and chlorophyll-α increased during the lockdown period due to the stagnation of water as well as a reduction in industrial and anthropogenic pollution. Moreover, the prediction of EWQI using an artificial neural network indicates that the overall water quality will improve more if the lockdown period is sustained for another 3 years. The outcomes of the study will help the stakeholders develop effective regulations and strategies for the timely restoration of lake water quality.