Potential hotspot modeling and monitoring of PM2.5 concentration for sustainable environmental health in Maharashtra, India

Assessment of ambient air quality in relation to the burning of firecrackers during the festival of Diwali: A case study of Jodhpur City (India)

The study attempts to examine the impact of firework activities during Diwali Festival on ambient air quality of Jodhpur city. Air quality parameters particulate matter of diameter 10 μm (PM10), particulate matter of diameter 2.5 μm (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2) and heavy metals in PM2.5 like Pb, Ni, Ba, Al, As and Sr are monitored at two locations, for 15 days, starting from 7 days before the festival of Diwali, on the day of the festival (Diwali) and 7 days after Diwali. On the occasion of Diwali, it was discovered that the 24-h average levels of various pollutants were significantly elevated compared to regular days preceding the festival. Specifically, at the HBO site, the concentrations were notably increased, with sulfur dioxide (SO2) reaching 5.62 times higher, nitrogen dioxide (NO2) at 3 times higher, particulate matter of diameter 10 μm (PM10) at 2.35 times higher, and particulate matter of diameter 2.5 μm (PM2.5) at 1.01 times higher than the usual levels before Diwali. Similarly, at the PTMM site, there were substantial elevations in pollutant concentrations during Diwali compared to pre-festival days, with SO2 registering 2.53 times higher, NO2 at 2.37 times higher, PM2.5 at 1.9 times higher, and PM10 at 1.57 times higher levels than normal. Concentration of Al, Ba, Sr and Pb at HBO site and Al at PTMM site was highest on Diwali day. Air quality index which was in good category on normal days before Diwali, fell into poor category starting from the day before Diwali and remain in poor category on normal days after Diwali. The result indicates the worsening of ambient air quality during Diwali which can adversely impact the human health in terms of various respiratory complications.

Development of geo-environmental factors controlled flash flood hazard map for emergency relief operation in complex hydro-geomorphic environment of tropical river, India

The occurrences of flash floods in sub-tropical climatic regions like India are ubiquitous phenomena, particularly during the monsoon season. This type of flood occurs within a short period of time and makes it distinctive from all-natural hazards, which causes huge loss of economy and causalities of life. Therefore, its prediction is crucial and one of the challenging tasks for researchers to mitigate this sustainably. Furthermore, identifying flash flood susceptible regions is the foremost responsibility in managing flood events, which helps the local administration take emergency relief operations in flood-prone regions. In September 2021, the flood in the Gandheswari river basin was the most severe compared to the past decade. The occurrences of flash floods in the lower course of the Gandheswari river has been affected riparian habitats rigorously. Thus, in this study, we proposed the bivariate logistic regression (LR) method to delineate this river basin's flash flood hazard (FFH) map. Here, sixteen flood conditioning factors were selected for modeling purposes with the help of a multicollinearity test, and a total of 71 flood points were identified from the historical dataset. The produced result was validated by six distinctive validating techniques, including receiver operating characteristics (ROC) analysis, specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and F-score. These techniques have shown that present modeling has high predictive performance in both training and testing dataset with the values of ROC (training-0.928, validating-0.892), specificity (training-0.911, validating-0.882), sensitivity (training-0.915, validating-0.885), PPV (training-0.912, validating-0.874), NPV (training-0.91, validating-0.875), and F-score (training-0.92, validating-0.89). Therefore, the proposed method in this and the outcome result will help the disaster manager make proper decisions to mitigate the hazardous situation and take sustainable emergency relief operations.

Change in the air due to the coronavirus outbreak in four major cities of India: What do the statistics say?

The onset of the novel Coronavirus (COVID-19) has impacted all sectors of society. To avoid the rapid spread of this virus, the Government of India imposed a nationwide lockdown in four phases. Lockdown, due to COVID-19 pandemic, resulted a decline in pollution in India in general and in dense cities in particular. Data on key air quality indicators were collected, imputed, and compiled for the period 1st August 2018 to 31st May 2020 for India's four megacities, namely Delhi, Mumbai, Kolkata, and Hyderabad. Autoregressive integrated moving average (ARIMA) model and machine learning technique e.g. Artificial Neural Network (ANN) with the inclusion of lockdown dummy in both the models have been applied to examine the impact of anthropogenic activity on air quality parameters. The number of indicators having significant lockdown dummy are six (PM_2.5, PM₁₀, NOx, CO, benzene, and AQI), five (PM_2.5, PM₁₀, NOx, SO₂ and benzene), five (PM₁₀, NOx, CO, benzene and AQI) and three (PM_2.5, PM₁₀, and AQI) for Delhi, Kolkata, Mumbai and Hyderabad respectively. It was also observed that the prediction accuracy significantly improved when a lockdown dummy was incorporated. The highest reduction in Mean Absolute Percentage Error (MAPE) is found for CO in Hyderabad (28.98%) followed by the NOx in Delhi (28.55%). Overall, it can be concluded that there is a significant decline in the value of air quality parameters in the lockdown period as compared to the same time phase in the previous year. Insights from the COVID-19 pandemic will help to achieve significant improvement in ambient air quality while keeping economic growth in mind.

Polycyclic aromatic hydrocarbons in urban particle matter exacerbate movement disorder after ischemic stroke via potentiation of neuroinflammation

BACKGROUND

A recent epidemiological study showed that air pollution is closely involved in the prognosis of ischemic stroke. We and others have reported that microglial activation in ischemic stroke plays an important role in neuronal damage. In this study, we investigated the effects of urban aerosol exposure on neuroinflammation and the prognosis of ischemic stroke using a mouse photothrombotic model.

RESULTS

When mice were intranasally exposed to CRM28, urban aerosols collected in Beijing, China, for 7 days, microglial activation was observed in the olfactory bulb and cerebral cortex. Mice exposed to CRM28 showed increased microglial activity and exacerbation of movement disorder after ischemic stroke induction. Administration of core particles stripped of attached chemicals from CRM28 by washing showed less microglial activation and suppression of movement disorder compared with CRM28-treated groups. CRM28 exposure did not affect the prognosis of ischemic stroke in null mice for aryl hydrocarbon receptor, a polycyclic aromatic hydrocarbon (PAH) receptor. Exposure to PM2.5 collected at Yokohama, Japan also exacerbated movement disorder after ischemic stroke.

CONCLUSION

Particle matter in the air is involved in neuroinflammation and aggravation of the prognosis of ischemic stroke; furthermore, PAHs in the particle matter could be responsible for the prognosis exacerbation.

Evaluating the effectiveness of lockdowns and restrictions during SARS-CoV-2 variant waves in the Canadian province of Nova Scotia

Introduction

After the initial onset of the SARS-CoV-2 pandemic, the government of Canada and provincial health authorities imposed restrictive policies to limit virus transmission and mitigate disease burden. In this study, the pandemic implications in the Canadian province of Nova Scotia (NS) were evaluated as a function of the movement of people and governmental restrictions during successive SARS-CoV-2 variant waves (i.e., Alpha through Omicron).

Methods

Publicly available data obtained from community mobility reports (Google), the Bank of Canada Stringency Index, the "COVID-19 Tracker" service, including cases, hospitalizations, deaths, and vaccines, population mobility trends, and governmental response data were used to relate the effectiveness of policies in controlling movement and containing multiple waves of SARS-CoV-2.

Results

Our results indicate that the SARS-CoV-2 pandemic inflicted low burden in NS in the initial 2 years of the pandemic. In this period, we identified reduced mobility patterns in the population. We also observed a negative correlation between public transport (-0.78), workplace (-0.69), retail and recreation (-0.68) and governmental restrictions, indicating a tight governmental control of these movement patterns. During the initial 2 years, governmental restrictions were high and the movement of people low, characterizing a 'seek-and-destroy' approach. Following this phase, the highly transmissible Omicron (B.1.1.529) variant began circulating in NS at the end of the second year, leading to increased cases, hospitalizations, and deaths. During this Omicron period, unsustainable governmental restrictions and waning public adherence led to increased population mobility, despite increased transmissibility (26.41-fold increase) and lethality (9.62-fold increase) of the novel variant.

Discussion

These findings suggest that the low initial burden caused by the SARS-CoV-2 pandemic was likely a result of enhanced restrictions to contain the movement of people and consequently, the spread of the disease. Easing public health restrictions (as measured by a decline in the BOC index) during periods of high transmissibility of circulating COVID-19 variants contributed to community spread, despite high levels of immunization in NS.

Hydrogeochemical characterization based water resources vulnerability assessment in India's first Ramsar site of Chilka lake

A limnological site is significantly characterized by rich biological, chemical, and physical properties of the environment and is also described as the epitome of a large aquatic ecosystem. During the last few decades, the Chilka lake Ramsar site has experienced substantial degradation of water quality with associated deterioration of aquatic biodiversity. Our study aims to quantify the VWRM of the Chilka lake Ramsar region using the most reliable MLAs, namely ANN and RF, with the help of seventeen hydro-chemical properties of lake water. The produced map is validated through six validating measures (ROC-AUC- 0.89, Sensitivity-0.90, Specificity-0.78, PPV-0.78, NPV-0.88, Taylor diagram (r)-0.94), which depict that ANN is the most reliable ML algorithm in assessing the VWRM of the concerned region followed by RF. The prepared map of our study revealed that the eastern part was remarkably high to very high vulnerable zone covered area with 22.41 % and 7.19 %, respectively.

Strict lockdown measures reduced PM2.5 concentrations during the COVID-19 pandemic in Kolkata, India

The COVID-19 situation is a critical state throughout the world that most countries have been forced to implement partial to total lockdown to control the COVID-19 disease outbreak. And displays the natural power to rejuvenate herself without the interference of human beings. So, the top-level emergency response including full quarantine actions are significant measures against the COVID-19 and resulted in a notable reduction in PM_2.5 in the atmosphere. India was severely attacked by COVID-19, and as a result, the Government of India has imposed a nationwide lockdown from 24th March (2020) to 30th May (2020) in different phases. The COVID-19 outbreak and lockdown had a significant negative impact on India's socioeconomic structure but had a positive impact on environmental sustainability in terms of improved air quality due to the 68 days of the shutdown of India's industrial, commercial, construction, and transportation systems. The current study looked at the spatio-temporal changes in PM_2.5 concentrations at different air quality monitoring stations (AQMS) in Kolkata during the COVID-19 period. The study revealed that the average concentration of PM_2.5 (µg/m³) was slightly high (139.82) in the pre-lockdown period which was rapidly reduced to 37.77 (72.99% reduction) during the lockdown period and it was further increased (137.11) in post-lockdown period. The study also shows that the average concentration of PM_2.5 was 66.83 in 2018, which slightly increased to 70.43 (5.39%) in 2019 and dramatically decreased to 37.77 (46.37%) in the year 2020 due to the COVID-19 outbreak and lockdown. The study clearly shows that air quality improves during lockdown periods in Kolkata, but it is not a permanent solution rather than temporary. Therefore, it is necessary to make the proper policies and strategies by policymakers and government authorities, and environmental scientists to maintain such good air quality by controlling several measures of air pollutants.