The electronics industry is one of fastest growing manufacturing industries in India. However, the increase in the sales of electronic goods and their rapid obsolescence has resulted in the large-scale generation of electronic waste, popularly known as e-waste. E-waste has become a matter of concern due to the presence of toxic and hazardous substances present in electronic goods which, if not properly managed, can have adverse effects on the environment and human health. In India, the e-waste market remains largely unorganized, with companies being neither registered nor authorized and typically operating on an informal basis. In many instances, e-waste is treated as municipal waste, because India does not have dedicated legislation for the management of e-waste. It is therefore necessary to review the public health risks and strategies in a bid to addressthis growing hazard. There is the strong need for adopting sustainability practices in order to tackle the growing threat of e-waste. In the present work, we attempt to identify the various sources and reasons for e-waste generation, in addition to understanding the perception of the public towards e-waste management. This study aims to induce an awareness of sustainability practices and sustainability issues in the management of E-waste, especially waste related to personal computers (PCs) and mobile phones. From the results of the study, we concluded that the majority (90%) of the public is ignorant about e-waste and its issues; hence, there is a strong requirement for spreading awareness about the growing hazard of E-waste.

Wastewater-based epidemiology (WBE) has emerged as a valuable approach for forecasting disease outbreaks in developed countries with a centralized sewage infrastructure. On the other hand, due to the absence of well-defined and systematic sewage networks, WBE is challenging to implement in developing countries like Bangladesh where most people live in rural areas. Identification of appropriate locations for rural Hotspot Based Sampling (HBS) and urban Drain Based Sampling (DBS) are critical to enable WBE based monitoring system. We investigated the best sampling locations from both urban and rural areas in Bangladesh after evaluating the sanitation infrastructure for forecasting COVID-19 prevalence. A total of 168 wastewater samples were collected from 14 districts of Bangladesh during each of the two peak pandemic seasons. RT-qPCR commercial kits were used to target ORF1ab and N genes. The presence of SARS-CoV-2 genetic materials was found in 98% (165/168) and 95% (160/168) wastewater samples in the first and second round sampling, respectively. Although wastewater effluents from both the marketplace and isolation center drains were found with the highest amount of genetic materials according to the mixed model, quantifiable SARS-CoV-2 RNAs were also identified in the other four sampling sites. Hence, wastewater samples of the marketplace in rural areas and isolation centers in urban areas can be considered the appropriate sampling sites to detect contagion hotspots. This is the first complete study to detect SARS-CoV-2 genetic components in wastewater samples collected from rural and urban areas for monitoring the COVID-19 pandemic. The results based on the study revealed a correlation between viral copy numbers in wastewater samples and SARS-CoV-2 positive cases reported by the Directorate General of Health Services (DGHS) as part of the national surveillance program for COVID-19 prevention. The findings of this study will help in setting strategies and guidelines for the selection of appropriate sampling sites, which will facilitate in development of comprehensive wastewater-based epidemiological systems for surveillance of rural and urban areas of low-income countries with inadequate sewage infrastructure.

In parts of developing countries, the over-exploitation of sands from inland waters has led to serious environmental concerns. However, understanding of the impacts of commercial sand dredging on inland water ecosystem functions remains limited. Herein, we assess the effects of this activity on the functional structure of the macroinvertebrate community and its recovery processes based on a 4-year survey, in the South Dongting Lake in China. Our result showed a simplified macroinvertebrate functional structures within the dredged area, as evidenced by a loss of certain trait categories (e.g., oval and conical body form) and a significant reduction in trait values due to the direct removal of macroinvertebrates and indirect alternations to physical environmental conditions (e.g., water depth and %Medium sand). Moreover, clear increases were observed in certain trait categories (e.g., small body size and swimmer) resulting from the dredging-related disturbance (e.g., increased turbidity) within the adjacent area. Furthermore, one year after the cessation of dredging, a marked recovery in the taxonomic and functional structure of macroinvertebrate assemblages was observed with most lost trait categories returning and an increase in the trait values of eight categories (e.g., body size 1.00–3.00 cm and oval body form) within the dredged and adjacent area. In addition, dispersal processes and sediment composition were the main driver for the structuring of the macroinvertebrate taxonomic and functional assemblages during the dredging stages, whilst water environmental conditions dominated the taxonomic structure and dispersal processes determined the functional structure during the recovery stage. Implications of our results for monitoring and management of this activity in inland waters are discussed.

Concrete, a cement-based product is the highest manufactured and second highest consumed product after water on earth. Across the world, production of cement is the most energy and emission intensive industry hence, the cement industry is currently under pressure to reduce greenhouse gases emissions (GHGEs). However, reducing the GHGEs of the cement industry especially for developing country like India is not an easy task. Cement manufacturing industry needs to focus on significant climate change mitigation strategies to reduce the GHGEs to sustain its production. This study aims at identifying significant climate change mitigation strategies of the cement manufacturing industry in the context of India. Extant literature review and expert opinion are used to identify climate change mitigation strategies of the cement manufacturing industry. In the present study, a model projects by applying both AHP and DEMATEL techniques to assess the climate change mitigation strategies of the cement industry. The AHP technique help in establishing the priorities of climate change mitigation strategies, while the DEMATEL technique forms the causal relationships among them. Through AHP, the results of this research demonstrate that Fuel emission reduction is on top most priority while the relative importance priority of the main remaining factors is Process emission reduction - Electric energy-related emission - Emission avoidance and reduction - Management mitigation measures. The findings also indicate that the main factors, Process emission reduction, and Fuel emission reduction are categorized in cause group factors, while the remaining factors, Electric energy-related emission, Emission avoidance and reduction and Management mitigation measures are in effect group factors. Present model will help supply chain analysts to develop both short-term and long-term decisive measures for effectively managing and reducing GHGEs.

Global water safety is facing great challenges due to increased population and demand. There is an urgent need to develop suitable water treatment strategy for small rural and remote communities in low-income developing countries. In order to find a low-cost solution, the reduction of E. coli using ceramic water disk coated with nano ZnO was investigated in this study. The performance of modified ceramic disk filters was influenced by several factors in the filter production process. Based on the factorial analysis, the pore size of the disk filters was the most significant factor for influencing E. coli removal efficiency and the clay content was the most significant one for influencing flow rate of modified disk filters. The coating of nano ZnO led to the change of disk filter surface and porosity. The reduction of E. coli could be attributed to both filter retention and photocatalytic antibacterial activity of nano ZnO. The effects of filter operation factors including initial E. coli concentration, illumination time and lamp power on E. coli removal effectiveness were also revealed. The results can help find a safe and cost-effective approach to solve drinking water problems in small rural and remote communities of developing regions.

Exposure to fine particulate matter (PM₂.₅) remains a worldwide public health issue. However, epidemiological studies on the chronic health impacts of PM₂.₅ in the developing countries are hindered by the lack of monitoring data. Despite the recent development of using satellite remote sensing to predict ground-level PM₂.₅ concentrations in China, methods for generating reliable historical PM₂.₅ exposure, especially prior to the construction of PM₂.₅ monitoring network in 2013, are still very rare. In this study, a high-performance machine-learning model was developed directly at monthly level to estimate PM₂.₅ levels in North China Plain. We developed a random forest model using the latest Multi-angle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD), meteorological parameters, land cover and ground PM₂.₅ measurements from 2013 to 2015. A multiple imputation method was applied to fill the missing values of AOD. We used 10-fold cross-validation (CV) to evaluate model performance and a separate time period, January 2016 to December 2016, was used to validate our model's capability of predicting historical PM₂.₅ concentrations. The overall model CV R² and relative prediction error (RPE) were 0.88 and 18.7%, respectively. Validation results beyond the modeling period (2013–2015) shown that this model can accurately predict historical PM₂.₅ concentrations at the monthly (R² = 0.74, RPE = 27.6%), seasonal (R² = 0.78, RPE = 21.2%) and annual (R² = 0.76, RPE = 16.9%) level. The annual mean predicted PM₂.₅ concentration from 2013 to 2016 in our study domain was 67.7 μg/m3 and Southern Hebei, Western Shandong and Northern Henan were the most polluted areas. Using this computationally efficient, monthly and high-resolution model, we can provide reliable historical PM₂.₅ concentrations for epidemiological studies on PM₂.₅ health effects in China.

Hormone active agents constitute a dangerous class of pollutants. Among them, those agents that mimic the action of estrogens on target cells and are part of the group of endocrine-disruptor compounds (EDCs) are termed estrogenic EDCs, the main focus of this review. Exposure to these compounds causes a number of negative effects, including breast cancer, infertility and animal hermaphroditism. However, especially in underdeveloped countries, limited efforts have been made to warn people about this serious issue, explain the methods of minimizing exposure, and develop feasible and efficient mitigation strategies at different levels and in various environments. For instance, the use of bioremediation processes capable of transforming EDCs into environmentally friendly compounds has been little explored. A wide diversity of estrogen-degrading microorganisms could be used to develop such technologies, which include bioremediation processes for EDCs that could be implemented in biological filters for the post-treatment of wastewater effluent. This review describes problems associated with EDCs, primarily estrogenic EDCs, including exposure as well as the present status of understanding and the effects of natural and synthetic hormones and estrogenic EDCs on living organisms. We also describe potential biotechnological strategies for EDC biodegradation, and suggest novel treatment approaches for minimizing the persistence of EDCs in the environment.

Child labor is a major challenge in the developing countries and comprehensive health hazard identification studies on this issue are still lacking. Therefore, the current study is an effort to highlight the health concerns of child labor exposed in the key small scale industries of Sialkot, Pakistan. Our findings revealed jolting levels of heavy metals in the urine, blood, serum, saliva, and hair samples collected from the exposed children. For example, in the urine samples, Cd, Cr, Ni, and Pb were measured at the respective concentrations of 39.17, 62.02, 11.94 and 10.53 μg/L in the surgical industries, and 2.10, 4.41, 1.04 and 5.35 μg/L in the leather industries. In addition, source apportionment revealed polishing, cutting, and welding sections in the surgical industries and surface coating, crusting, and stitching sections in the leather industries were the highest contributors of heavy metals in the bio-matrices of the exposed children, implying the dusty, unhygienic, and unhealthy indoor working conditions. Further, among all the bio-matrices, the hair samples expressed the highest bioaccumulation factor for heavy metals. In accordance with the heavy metal levels reported in the exposed children, higher oxidative stress was found in the children working in the surgical industries than those from the leather industries. Moreover, among heavy metals’ exposure pathways, inhalation of industrial dust was identified as the primary route of exposure followed by the ingestion and dermal contact. Consequently, chemical daily intake (CDI), carcinogenic and non-carcinogenic hazard quotients (HQs) of heavy metals were also reported higher in the exposed children and were also alarmingly higher than the corresponding US EPA threshold limits. Taken all together, children were facing serious health implications in these industries and need immediate protective measures to remediate the current situation.

Ambient air pollution ranks high among the risk factors that increase the global burden of disease. Previous studies focused on assessing mortality risk and were sparsely performed in populous developing countries with deteriorating environments. We conducted a time-series study to evaluate the air pollution-associated years of life lost (YLL) and mortality risk and to identify potential modifiers relating to the season and demographic characteristics. Using linear (for YLL) and Poisson (for mortality) regression models and controlling for time-varying factors, we found that an interquartile range (IQR) increase in a three-day average cumulative (lag 0–2 day) concentrations of PM2.5, PM10, NO2 and SO2 corresponded to increases in YLL of 12.09 (95% confidence interval [CI]: 2.98–21.20), 13.69 (95% CI: 3.32–24.07), 26.95 (95% CI: 13.99–39.91) and 24.39 (95% CI: 8.62–40.15) years, respectively, and to percent increases in mortality of 1.34% (95% CI: 0.67–2.01%), 1.56% (95% CI: 0.80–2.33%), 3.36% (95% CI: 2.39–4.33%) and 2.39% (95% CI: 1.24–3.55%), respectively. Among the specific causes of death, cardiovascular and respiratory diseases were positively associated with gaseous pollutants (NO2 and SO2), and diabetes was positively correlated with NO2 (in terms of the mortality risk). The effects of air pollutants were more pronounced in the cool season than in the warm season. The elderly (>65 years) and females were more vulnerable to air pollution. Studying effect estimates and their modifications by using YLL to detect premature death should support implementing health risk assessments, identifying susceptible groups and guiding policy-making and resource allocation according to specific local conditions.

Atmospheric acid deposition is a global environmental issue. China has been experiencing serious acid deposition, which is anticipated to become more severe with the country's economic development and increasing consumption of fossil fuels in recent decades. We explored the spatiotemporal variations of acid deposition (wet acid deposition) and its influencing factors by collecting nationwide data on pH and concentrations of sulfate (SO4²⁻) and nitrate (NO3⁻) in precipitation between 1980 and 2014 in China. Our results showed that average precipitation pH values were 4.59 and 4.70 in the 1990s and 2010s, respectively, suggesting that precipitation acid deposition in China has not seriously worsened. Average SO4²⁻ deposition declined from 40.54 to 34.87 kg S ha⁻¹ yr⁻¹ but average NO3⁻ deposition increased from 4.44 to 7.73 kg N ha⁻¹ yr⁻¹. Specifically, the area of severe precipitation acid deposition in southern China has shrunk to some extent as a result of controlling the pollutant emissions; but the area of moderate precipitation acid deposition has expanded in northern China, associated with rapid industrial and transportation development. Furthermore, we found significant positive correlations between precipitation acid deposition, energy consumption, and rainfall. Our findings provide a relatively comprehensive evaluation of the spatiotemporal dynamics of precipitation acid deposition in China over past three decades, and confirm the idea that strategies implemented to save energy and control pollutant emissions in China have been effective in alleviating precipitation acid deposition. These findings might be used to demonstrate how developing countries could achieve economic development and environmental protection through the implementation of advanced technologies to reduce pollutant emissions.