Estimating ground-level PM2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh.

Fine particulate matter (PM2.5) has become a prominent pollutant due to rapid economic development, urbanization, industrialization, and transport activities, which has serious adverse effects on human health and the environment. Many studies have employed traditional statistical models and remote-sensing technologies to estimate PM2.5 concentrations. However, statistical models have shown inconsistency in PM2.5 concentration predictions, while machine learning algorithms have excellent predictive capacity, but little research has been done on the complementary advantages of diverse approaches. The present study proposed the best subset regression model and machine learning approaches, including random tree, additive regression, reduced error pruning tree, and random subspace, to estimate the ground-level PM2.5 concentrations over Dhaka. This study used advanced machine learning algorithms to measure the effects of meteorological factors and air pollutants (NOX, SO2, CO, and O3) on the dynamics of PM2.5 in Dhaka from 2012 to 2020. Results showed that the best subset regression model was well-performed for forecasting PM2.5 concentrations for all sites based on the integration of precipitation, relative humidity, temperature, wind speed, SO2, NOX, and O3. Precipitation, relative humidity, and temperature have negative correlations with PM2.5. The concentration levels of pollutants are much higher at the beginning and end of the year. Random subspace is the optimal model for estimating PM2.5 because it has the least statistical error metrics compared to other models. This study suggests ensemble learning models to estimate PM2.5 concentrations. This study will help quantify ground-level PM2.5 concentration exposure and recommend regional government actions to prevent and regulate PM2.5 air pollution. Supplementary Information: The online version contains supplementary material available at 10.1007/s11869-023-01329-w.

Wetland health assessment using DPSI framework: a case study in Kolkata Metropolitan Area.

Wetlands are among the most valuable components of the ecosystem, playing an important role in preventing floods, maintaining the hydrological cycle, protecting against natural hazards, and controlling local weather conditions and ecological restoration. The Kolkata Metropolitan Area (KMA) is considered one of the most ecologically valuable regions in terms of wetland ecosystem, but due to haphazard development and human activities, the wetlands of the city are under constant threat of degradation. Therefore, this study aims to assess the factors responsible for wetland health and their dynamics using Driving Force-Pressure-State-Impact (DPSI) framework. To assess wetland health during 2011-2020, seventeen indicators and four sub-indicators were selected to calculate weights using the analytic hierarchy process (AHP). The results showed that most of the municipalities in the healthy category were in the pressure (P) section in 2011, while fluctuations were observed in the impact (I) section in several wards during 2011-20. The condition section (S) showed the overall change in the water, vegetation, and built-up categories from 2011 to 2020, so the most dominant category was "healthy," followed by "unhealthy" and "poor." The highly significant factors worsening wetland health were population density (B1), road density (B3), per capita wastewater generation (B5), per capita solid waste generation (B7), biological oxygen demand (D1a), dissolved oxygen (D1b), pH (D1c), and total coliform (D1d). The results of the study can help develop sustainable conservation and management of the wetland ecosystem in the KMA urban area and at the global level with similar geographical conditions.

Wildfire-induced pollution and its short-term impact on COVID-19 cases and mortality in California.

Globally, wildfires have seen remarkable increase in duration and size and have become a health hazard. In addition to vegetation and habitat destruction, rapid release of smoke, dust and gaseous pollutants in the atmosphere contributes to its short and long-term detrimental effects. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has emerged as a public health concern worldwide that primarily target lungs and respiratory tract, akin to air pollutants. Studies from our lab and others have demonstrated association between air pollution and COVID-19 infection and mortality rates. However, current knowledge on the impact of wildfire-mediated sudden outburst of air pollutants on COVID-19 is limited. In this study, we examined the association of air pollutants and COVID-19 during wildfires burned during August-October 2020 in California, United States. We observed an increase in the tropospheric pollutants including aerosols (particulate matter [PM]), carbon monoxide (CO) and nitrogen dioxide (NO2) by approximately 150%, 100% and 20%, respectively, in 2020 compared to the 2019. Except ozone (O3), similar proportion of increment was noticed during the peak wildfire period (August 16 - September 15, 2020) in the ground PM2.5, CO, and NO2 levels at Fresno, Los Angeles, Sacramento, San Diego and San Francisco, cities with largest active wildfire area. We identified three different spikes in the concentrations of PM2.5, and CO for the cities examined clearly suggesting wildfire-induced surge in air pollution. Fresno and Sacramento showed increment in the ground PM2.5, CO and NO2 levels, while San Diego recorded highest change rate in NO2 levels. Interestingly, we observed a similar pattern of higher COVID-19 cases and mortalities in the cities with adverse air pollution caused by wildfires. These findings provide a logical rationale to strategize public health policies for future impact of COVID-19 on humans residing in geographic locations susceptible to sudden increase in local air pollution.

Increased tropospheric ozone levels as a public health issue during COVID-19 lockdown and estimation the related pulmonary diseases.

The aims of this study were to i) investigate the variation of tropospheric ozone (O3) levels during the COVID-19 lockdown; ii) determine the relationships between O3 concentrations with the number of COVID-19 cases; and iii) estimate the O3-related health effects in Southwestern Iran (Khorramabad) over the time period 2019-2021. The hourly O3 data were collected from ground monitoring stations, as well as retrieved from Sentinel-5 satellite data for showing the changes in O3 levels pre, during, and after lockdown period. The concentration-response function model was applied using relative risk (RR) values and baseline incidence (BI) to assess the O3-related health effects. Compared to 2019, the annual O3 mean concentrations increased by 12.2% in 2020 and declined by 3.9% in 2021. The spatiotemporal changes showed a significant O3 increase during COVID-19 lockdown, and a negative correlation between O3 levels and the number of COVID-19 cases was found (r = - 0.59, p < 0.05). In 2020, the number of hospital admissions for cardiovascular diseases increased by 4.0 per 105 cases, the mortality for respiratory diseases increased by 0.7 per 105 cases, and the long-term mortality for respiratory diseases increased by 0.9 per 105 cases. Policy decisions are now required to reduce the surface O3 concentrations and O3-related health effects in Iran.

Analysing the role of gender and place of residence in acceptability and satisfaction towards e-learning among university students' during COVID-19 pandemic in India.

This paper has two broad objectives; the first is to examine the challenges of e-learning faced by the students keeping in view their place of residence and gender in India, particularly during the second-wave of Covid-19. The second objective is to examine the role of place of residence and gender of students in the acceptance and satisfaction towards e-learning. The data has been obtained through an online survey of the students of University of Jamia Millia Islamia, New Delhi, India, in which a total of 490 students participated. Selection of students has been done through stratified sampling technique. Initially the obtained data was analysed and discussed through simple statistical analysis. Later, a chi-square test of independence was applied to find out the dependency of psychological stress, level of acceptance and level of satisfaction towards e-learning on the place of residence and the gender. The major finding of the paper reveals that the gender and the place of residence of the students is significantly associated with their psychological stress, acceptance and satisfaction towards e-learning. Extra money spent on the purchase of online learning resources was greater in case of rural students.

Challenges of unrestricted assignment-based examinations (ABE) and restricted open-book examinations (OBE) during COVID-19 pandemic in India: An experimental comparison.

COVID-19 pandemic has affected every sphere of life specially the education sector observing a paradigm shift in the nature of pedagogy from offline face-to-face to online-virtual mode of learning. The biggest challenge in online-learning was the conduction of online examination for student's assessment specially in Indian context where digital divide is rampant. Thus, present study examines and compares the challenges faced by the students in two most widely accepted modes of examination by Indian universities and institutes of higher learning, that is, take home/unrestricted/assignment-based examination (ABE) and highly time restricted/open-book examination (OBE). Primary data was collected through questionnaires prepared by using Google forms to measure adaptability, satisfaction, and challenges using 5-point Likert's scale. Cronbach's α test was performed on question items to check the reliability and internal consistency of the items. χ 2 test has been applied in order to check whether there is a statistically significant relationship between the gender and place of residence in the acceptability of ABE and OBE. The findings suggest that both modes of examination have their own challenges largely governed by the digital and economic divide. The acceptance level of ABE and OBE is not associated with gender. However, we found the level of acceptance association of ABE with the place of residence of the students but not with OBE.

Spatio-temporal analysis of air quality and its relationship with major COVID-19 hotspot places in India.

The COVID-19 pandemic spread worldwide, such as wind, with more than 400,000 documented cases as of March 24th, 2020. In this regard, strict lockdown measures were imposed in India on the same date to stop virus spread. Thereafter, various lockdown impacts were observed, and one of the immediate effects was a reduction in air pollution levels across the world and in India as well. In this study, we have observed approximately 40% reduction in air quality index (AQI) during one month of lockdown in India. The detailed investigations were performed for 14 major hotspot places where the COVID-19 cases were >1000 (as of 1st June 2020) and represents more than 70% associated mortality in India. We assessed the impact of lockdown on different air quality indicators, including ground (PM2.5, PM10, NO2, SO2, O3, and AQI) and tropospheric nitric oxide (NO2) pollutants, through ground monitoring stations and Sentinel-5 satellite datasets respectively. The highest reductions were noticed in NO2 (-48.68%), PM2.5 (-34.84%) and PM10 (-33.89%) air pollutant (unit in µg/m3) post-lockdown. Moreover, tropospheric NO2 (mol/m2) concentrations were also improved over Delhi, Mumbai, Kolkata, Thane, and Ahmedabad metro cities. We found strong positive correlation of COVID-19 mortality with PM10 (R2 = 0.145; r = 0.38) and AQI (R2 = 0.17; r = 0.412) pollutant indicators that significantly improved next time point. The correlation finding suggests that long-term bad air quality may aggravate the clinical symptoms of the disease.

Improved air quality and associated mortalities in India under COVID-19 lockdown.

India enforced stringent lockdown measures on March 24, 2020 to mitigate the spread of the Severe Acute Respiratory Syndrome Coronovirus-2 (SARS-CoV-2). Here, we examined the impact of lockdown on the air quality index (AQI) [including ambient particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3), and ammonia (NH3)] and tropospheric NO2 and O3 densities through Sentinel-5 satellite data approximately 1 d post-lockdown and one month pre-lockdown and post-lockdown. Our findings revealed a marked reduction in the ambient AQI (estimated mean reduction of 17.75% and 20.70%, respectively), tropospheric NO2 density, and land surface temperature (LST) during post-lockdown compared with the pre-lockdown period or corresponding months in 2019, except for a few sites with substantial coal mining and active power plants. We observed a modest increase in the O3 density post-lockdown, thereby indicating improved tropospheric air quality. As a favorable outcome of the COVID-19 lockdown, road accident-related mortalities declined by 72-folds. Cities with poor air quality correlate with higher COVID-19 cases and deaths (r = 0.504 and r = 0.590 for NO2; r = 0.744 and r = 0.435 for AQI). Conversely, low mortality was reported in cities with better air quality. These results show a correlation between the COVID-19 vulnerable regions and AQI hotspots, thereby suggesting that air pollution may exacerbate clinical manifestations of the disease. However, a prolonged lockdown may nullify the beneficial environmental outcomes by adversely affecting socioeconomic and health aspects.

Data on time series analysis of land surface temperature variation in response to vegetation indices in twelve Wereda of Ethiopia using mono window, split window algorithm and spectral radiance model.

In the past, decadal time-series analysis has been done traditionally using meteorological data. In particular, decadal analysis of land surface temperature has been a major issue due to the unavailability of remote sensing techniques. But, nowadays, with the recent advances in remote sensing techniques and modern software Land Surface Temperature (LST) can be calculated through the thermal bands. LST can be estimated through many algorithms such as Split-window, Mono-Window (SW), Single-Channel (SH), among others. LST was estimated using Mono-Window algorithm on Landsat-5 TM, Landsat-7 ETM+ and split window algorithm on Landsat-8 OLI/TIRS Thermal Infrared (TIR) bands. Vegetation index was obtained by using Normalized Difference Vegetation Index (NDVI) from red and Near-Infrared (NIR) bands. NDVI has been effectively used in vegetation monitoring and to analyze the vegetation in responses to climate change such as surface temperature variation. The twelve Weredas (third-level administrative divisions) of Ethiopia which are highly prone to drought were selected to investigate decadal land surface temperature variations and its impact on the surrounding environment, especially on vegetation cover. Ten Landsat images of three different sensors from 1999 to 2018 were used as the basic data source. The processed data of surface temperature and vegetation indices showed a strong correlation. The higher LST values indicate the smaller NDVI and vice versa and it is also identified the areas with high temperature being barren regions and areas with low temperature covered with more vegetation.

Evaluation of data accuracies within a comprehensive geospatial-health data surveillance platform: SOMAARTH Demographic Development and Environmental Surveillance Site, Palwal, Haryana, India.

Evidence exists of an increasing prevalence of chronic conditions within developed and developing nations, notably for priority population groups. The need for the collection of geospatial data to monitor the health impact of rapid social-environmental and economic changes occurring in these countries is being increasingly recognized. Rigorous accuracy assessment of such geospatial data is required to enable error estimation, and ultimately, data utility for exploring population health. This research outlines findings from a field-based evaluation exercise of the SOMAARTH DDESS geospatial-health platform. Participatory-based mixed methods have been employed within Palwal-India to capture villager perspectives on built infrastructure across 51 villages. This study, conducted in 2013, included an assessment of data element position and attribute accuracy undertaken in six villages, documenting mapping errors and land parcel changes. Descriptive analyses of 5.1% (n = 455) of land parcels highlighted some discrepancies in position (6.4%) and attribute (4.2%) accuracy, and land parcel changes (17.4%). Furthermore, the evaluation led to a refinement of the existing geospatial health platform incorporating ground-truthed reflections from the participatory field exercise. The evaluation of geospatial data accuracies contributes to understandings on global public health surveillance systems, outlining the need to systematically consider assessment of environmental features in relation to lifestyle-related diseases.