The contributions of nations, authors, and high-output journals in COVID-19 and atmospheric contamination research, spanning from the commencement of 2020 to the conclusion of 2022, were investigated by researchers, drawing data from the Web of Science Core Collection (WoS). Publications on the COVID-19 pandemic and air pollution totaled 504, attracting 7495 citations. (a) China showcased a substantial contribution (151 publications, 2996% of global output), playing a key role within the international research collaboration network, followed by India (101 publications, 2004% of global output) and the USA (41 publications, 813% of global output). (b) The urgent need for many studies stems from the widespread air pollution affecting China, India, and the USA. The considerable increase in research in 2020 led to a peak in publications in 2021, which then dropped in 2022. The author's focus on keywords has revolved around PM2.5, COVID-19, air pollution, and lockdown. Air pollution's impact on health, policy measures for air pollution control, and the improvement of air quality measurement are the primary research focuses implied by these keywords. These countries' COVID-19 social lockdown served as a meticulously crafted process for lessening air pollution. bioimpedance analysis In spite of this, the paper offers concrete advice for future research initiatives and a model for environmental and public health researchers to scrutinize the likely impact of COVID-19 social quarantines on urban air pollution.

Northeastern India's mountainous areas boast pristine, life-supporting streams, a vital resource for communities facing the persistent challenges of water scarcity, particularly in rural areas. The substantial degradation of stream water quality in the Jaintia Hills region, Meghalaya, during recent decades, primarily due to coal mining, necessitates a study assessing the spatiotemporal variation in stream water chemistry, particularly its response to acid mine drainage (AMD). To understand the state of water variables at each sampling point, principal component analysis (PCA) was employed as a multivariate statistical method, with the comprehensive pollution index (CPI) and water quality index (WQI) used to assess the water quality. Station S4 (54114) saw the peak WQI during the summer season, with the lowest WQI recorded at station S1 (1465) during the winter. Across various seasons, the WQI indicated good water quality for S1 (unimpacted stream). In contrast, impacted streams S2, S3, and S4 registered a markedly poor to completely unfit-for-consumption water status. Likewise, S1's CPI fell within the 0.20-0.37 range, signifying a water quality status of Clean to Sub-Clean, whereas the impacted streams' CPI values demonstrated a severely polluted condition. In addition, the PCA bi-plot revealed a higher affinity for free CO2, Pb, SO42-, EC, Fe, and Zn in AMD-affected streams as opposed to those that remained unimpacted. Acid mine drainage (AMD) in stream water, a key consequence of coal mine waste, demonstrates the environmental problems in the Jaintia Hills mining regions. Practically speaking, the government should create measures to reduce and stabilize the impact of the mine on the water bodies' well-being, understanding that stream water will remain the principal source of water for the tribal communities.

River dams, a source of economic gain for local production, are frequently perceived as environmentally beneficial. Recent studies have, however, indicated that the building of dams has led to the development of perfect conditions for methane (CH4) production in rivers, thereby altering their role from a weak riverine source to a powerful dam-associated one. From a temporal and spatial perspective, reservoir dams have a profound effect on the amount of methane released into the rivers within their region. Reservoir sedimentary layers and water level variations are the principal determinants of methane generation, operating through direct and indirect mechanisms. Water level regulation at the reservoir dam, interacting with environmental factors, leads to considerable changes in the water body's contents, affecting the production and movement of methane. The generated CH4 is ultimately discharged into the atmosphere through important emission modes, these being molecular diffusion, bubbling, and degassing. Global warming is, in part, fueled by methane (CH4) escaping from reservoir dams, a fact that cannot be overlooked.

Examining foreign direct investment (FDI) as a potential solution to lower energy intensity in developing countries between 1996 and 2019 is the aim of this research. A generalized method of moments (GMM) estimator was employed to investigate the linear and non-linear effects of FDI on energy intensity, with a focus on the interactive impact of FDI and technological progress (TP). Direct and substantial effects of FDI on energy intensity are revealed by the results, complemented by evidence of energy-saving technological transfers. The potency of this phenomenon is contingent upon the state of technological development within the less-developed world. testicular biopsy The validity of the research findings was underscored by the corroborative results of the Hausman-Taylor and dynamic panel data estimations and the parallel analysis of disaggregated data categorized by income levels. Based on the research, policy recommendations are designed to bolster FDI's potential for diminishing energy intensity in developing countries.

To advance exposure science, toxicology, and public health research, monitoring air contaminants is crucial. Air contaminant monitoring frequently suffers from missing data points, particularly in resource-limited contexts, including power disruptions, calibration procedures, and sensor malfunctions. The evaluation of existing imputation techniques for dealing with recurring instances of missing and unobserved data in contaminant monitoring is restricted. This proposed study will statistically evaluate six univariate and four multivariate time series imputation methods. The correlation structure over time forms the basis of univariate analyses, whereas multivariate approaches use multiple sites to complete missing data. The present study obtained data from 38 Delhi monitoring stations focused on particulate pollutants for a four-year duration. Under univariate methods, the simulation of missing values encompassed a range from 0% to 20% (5%, 10%, 15%, and 20%), and higher levels of 40%, 60%, and 80% missing values, marked by significant data gaps. Multivariate methods were preceded by preliminary steps on the input data. These steps encompassed choosing the target station for imputation, selecting covariates in consideration of spatial correlation across various locations, and creating a set of target and neighboring stations (covariates) with proportions of 20%, 40%, 60%, and 80%. Four multivariate methods are subsequently applied to the particulate pollution data encompassing a period of 1480 days. In conclusion, each algorithm's performance was gauged by employing error metrics. Results show an enhancement in outcomes for both univariate and multivariate time series analyses, arising from the extensive duration of the time series and the spatial correlations among the multiple data points from different locations. The univariate Kalman ARIMA model demonstrates strong performance in handling extended missing data, effectively addressing various missing values (except for 60-80%), resulting in low error rates, high R-squared values, and strong d-statistic. While Kalman-ARIMA fell short, multivariate MIPCA outperformed it at every target station with the maximum percentage of missing values.

Public health concerns and the spread of infectious diseases are intensified by the effects of climate change. this website Endemic to Iran, malaria is an infectious disease whose transmission is closely correlated with the climate. The simulation of climate change's impact on malaria in southeastern Iran, from 2021 to 2050, was performed using artificial neural networks (ANNs). Future climate models, generated under two contrasting scenarios (RCP26 and RCP85), were predicated on the optimal delay time, determined through the application of Gamma tests (GT) and general circulation models (GCMs). In order to model the varied repercussions of climate change on malaria infection, daily data collected from 2003 to 2014 (covering a 12-year period) were subjected to artificial neural network (ANN) analysis. A substantial temperature increase is predicted for the study area's climate by the year 2050. Under the RCP85 climate scenario, simulations of malaria cases unveiled a marked upward trajectory in infection rates, reaching a peak in 2050, concentrated within the warmest months of the year. The most significant input variables affecting the outcome were found to be rainfall and maximum temperature. Favorable temperatures and increased rainfall create an environment ideal for parasite transmission, resulting in a pronounced escalation of infection cases approximately 90 days later. ANNs were created as a practical method to simulate the consequences of climate change on malaria's prevalence, geographic distribution, and biological function. This enabled the estimation of future trends for appropriate preventive measures in endemic locations.

The advanced oxidation process, specifically sulfate radical-based (SR-AOPs), has been validated as a viable solution for treating persistent organic compounds in water, employing peroxydisulfate (PDS). A Fenton-like process, actively supported by visible-light-assisted PDS activation, proved highly effective in removing organic pollutants. Thermo-polymerization produced g-C3N4@SiO2, which was characterized using a range of techniques, including powder X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX), X-ray photoelectron spectroscopy (XPS), nitrogen adsorption-desorption isotherms with BET and BJH methods, photoluminescence (PL), transient photocurrent response, and electrochemical impedance measurements.