Bio-energy potential of available livestock waste and surplus agriculture crop residue: An analysis of 602 rural districts of India.

The majority of households in rural India use cow dung and crop residue for cooking, which contributes to both indoor and outdoor air pollution. After being used for cooking and other agricultural purposes, surplus crop residue left uncollected and burned openly accountable for notorious air pollution episodes in India. Air pollution and clean energy are critical challenges in India. Utilizing locally available biomass waste can be a sustainable solution to reducing air pollution and energy poverty. However, formulating any such policy and its practical implementation requires a clear understanding of currently available resources. The current study presents the first district-scale analysis of the cooking energy potential of locally available biomass (livestock and crop waste) if converted to energy by anaerobic digestion processes for 602 rural districts. The analysis indicates that rural India needs 1927TJ/day (2.75 MJ/capita-day) energy to meet the cooking energy demand. Utilizing locally available livestock waste can generate 715 TJ/day (1.02 MJ/capita-day) of energy, equivalent to 37 % of the demand. Only 2.15 % of districts have 100 % potential for cooking energy demand by utilizing locally produced livestock waste. Using surplus crop residue for energy can provide 2296 TJ/day(3.27 MJ/capita-day) of energy. If locally utilized, surplus residue can meet 100 % of energy demand in 39 % of districts. Combining livestock waste and surplus residue can produce 3011 TJ/day(4.29 MJ/capita-day) of energy, fulfilling >100 % of energy demand in 55.6 % of rural districts. Furthermore, converting agricultural waste into clean energy has the potential to reduce PM2.5 emissions by 33 % to 85 % in different scenarios.

High-Resolution PM2.5 Emissions and Associated Health Impact Inequalities in an Indian District.

Health and livelihood impacts from ambient air pollution among populations in developing countries are disproportional. These disparities are often overlooked due to a lack of information on microlevel emission data, especially in smaller cities and rural areas. The current work in an Indian district, Saharanpur, proposes the use of novel data sets to estimate microlevel emissions from air-polluting infrastructure sectors in urban and rural areas for use in pollutant transport models. Health impacts estimated based on the surface PM2.5 concentration suggest that the rate of premature deaths is 158 (95% CI: 122-163) and 143 (95% CI: 65-151) deaths per 100 000 people in urban and rural areas, respectively. Sixty-eight percent of the 6372 (95% CI: 3321-6987) annual premature deaths occurs in rural areas. Depicting higher contribution-exposure disparities among socioeconomic groups, the study observed that compared to their contribution to air pollution, low socioeconomic status (SES) groups in the region experience 6,7, 7, and 26% more premature deaths from PM2.5 exposure for industries, household cooking fuel burning, open waste burning, and transportation, respectively. The majority of disability-adjusted life years (DALYs) in the study domain are observed in economically weaker worker categories. Reduced income due to the loss of these life years will significantly impact these groups due to their dependence on daily wages for basic life necessities. Microlevel pollution mitigation policies with a focus on these inequalities are critical for promoting environmental equity and justice.

High resolution vehicular exhaust and non-exhaust emission analysis of urban-rural district of India.

Air quality deterioration due to vehicular emissions in smaller Indian cities and rural areas remains unacknowledged, even though the situation is alarmingly similar to megacities. The resulting lack of knowledge on travel behavior and vehicle characteristics impacts accuracy of emission studies in these regions. This study uses a novel approach and appropriate primary and secondary data sets to allocate vehicular activities (vehicle population and vehicle kilometer travelled) and associated emissions at a high spatial resolution for estimation and dispersion analysis of vehicular exhaust and non-exhaust PM2.5 emission in an Indian urban-rural landscape. The study indicates that using approaches that do not allocate vehicles kilometers travelled to areas of their expected travel results in underestimating the percent share of PM2.5 emissions from rural roads and motorways while overestimating overall PM2.5 emissions. Particulate matter resuspension is the dominant form of PM2.5 emissions from the vehicular sector on all road types, constituting an even higher fraction on rural roads. Two-wheelers contribute a high fraction of PM2.5 emissions (exhaust and non-exhaust combined), followed by heavy commercial vehicles and four-wheelers on urban roads. Light commercial vehicles, especially agricultural tractors dominate these emissions on rural roads. PM2.5 hotspots are prevalent in urban areas, but several rural areas also experience heavy particulate matter concentrations. Thus, vehicle movement incorporation results in more accurate emission estimation, especially in an urban-rural landscape.

All urban areas' energy use data across 640 districts in India for the year 2011.

India is the third-largest contributor to global energy-use and anthropogenic carbon emissions. India's urban energy transitions are critical to meet its climate goals due to the country's rapid urbanization. However, no baseline urban energy-use dataset covers all Indian urban districts in ways that align with national totals and integrate social-economic-infrastructural attributes to inform such transitions. This paper develops a novel bottom-up plus top-down approach, comprehensively integrating multiple field surveys and utilizing machine learning, to model All Urban areas' Energy-use (AllUrE) across all 640 districts in India, merged with social-economic-infrastructural data. Energy use estimates in this AllUrE-India dataset are evaluated by comparing with reported energy-use at three scales: nation-wide, state-wide, and city-level. Spatially granular AllUrE data aggregated nationally show good agreement with national totals (<2% difference). The goodness-of-fit ranged from 0.78-0.95 for comparison with state-level totals, and 0.90-0.99 with city-level data for different sectors. The relatively strong alignment at all three spatial scales demonstrates the value of AllUrE-India data for modelling urban energy transitions consistent with national energy and climate goals.

Epidemiology and diagnosis, environmental resources quality and socio-economic perspectives for COVID-19 pandemic.

The COVID-19 pandemic caused by SARS-CoV-2 has emerged as a global issue of concern for public health, environment and socio-economic setup. This review addresses several aspects of epidemiology, and pathogenesis, environmental resource quality (air quality, hazardous waste management, and wastewater surveillance issues), and socio-economic issues worldwide. The accelerated research activity in the development of diagnostic kits for SARS-CoV-2 is in progress for the rapid sequencing of various strains of SARS-CoV-2. A notable reduction in air pollutants (NO2 and PM2.5) has been observed worldwide, but high air polluted cities showed intense mortalities in COVID-19 affected areas. The use of health safety equipment halted transportation, and work-from-home policy drastically impacted the quantity of solid and hazardous wastes management services. Wastewater appeared as another mode of enteric transmission of SARS-CoV-2. Thus, wastewater-based surveillance could act as a mode of the data source to track the virus's community spread. The pandemic also had a substantial socio-economic impact (health budget, industrial manufacturing, job loss, and unemployment) and further aggravated the countries' economic burden.

Future liasing of the lockdown during COVID-19 pandemic: The dawn is expected at hand from the darkest hour.

The lockdown during COVID-19 pandemic has converted the world into new experimental laboratories, which may reveal temporal or spatial comparative analysis data. However, some startling information is gathered in terms of reduced premature mortality cases associated with air and water quality improvement, enhanced e-learning on a broader platform, work from home, and successful e-health. The decline in vehicular density on roads and congestion leads to reduced energy consumption and associated greenhouse gases (GHG) and other pollutants emission. The lockdown has also been identified as a possible emergency measure to combat severe air pollution episodes. Similarly, industrial pollution has been recognized as one of the primary causes of water resource pollution and would, therefore, bring change in policy vis-à-vis groundwater pollution control. Our findings suggest that the results of successful e-learning and work from home would be a permanent shift from conventional modes in the near future due to a drastic reduction in socio-economic cost. Our critical analysis also highlights that with such temporary lockdown measures acute/chronic ill-effects of anthropogenic perturbations on planet earth can be effectively estimated through sociocultural, socioeconomical and socio-political/sociotechnological nexus.

Exploring social and infrastructural factors affecting open burning of municipal solid waste (MSW) in Indian cities: A comparative case study of three neighborhoods of Delhi.

Open municipal solid waste (MSW)-burning is a major source of particulate matter emissions in developing world cities. Despite a legal ban, MSW-burning is observed ubiquitously in Indian cities with little being known about the factors shaping it. This study seeks to uncover social and infrastructural factors that affect MSW-burning at the neighborhood level. We couple physical assessments of the infrastructure provision and the MSW-burning incidences in three different neighborhoods of varying socio-economic status in Delhi, with an accompanying study of the social actors (interviews of waste handlers and households) to explore the extent to which, and potential reasons why, MSW-burning occurs. The observed differences in MSW-burning incidences range from 130 km-2 day-1 in low-income to 30 km-2 day-1 in the high-income areas. However, two high-income areas neighborhoods with functional infrastructure service also showed statistical differences in MSW-burning incidences. Our interviews revealed that, while the waste handlers were aware of the health risks associated with MSW-burning, it was not a high priority in the context of the other difficulties they faced. The awareness of the legal ban on MSW-burning was low among both waste handlers and households. In addition to providing infrastructure for waste pickup, informal restrictions from residents and neighborhood associations can play a significant role in restricting MSW-burning at the neighborhood scale. A more efficient management of MSW requires a combined effort that involves interplay of both social and infrastructural systems.

Characterizing the Spatial and Temporal Patterns of Open Burning of Municipal Solid Waste (MSW) in Indian Cities.

Open-burning of municipal solid waste (MSW) is a major source of PM emissions in developing world cities, but few studies have characterized this phenomenon at the city and intracity (neighborhood) scale relevant to human health impacts. This paper develops a consistent field method for measuring the spatial frequency of the incidence of MSW-burning and presents results in three neighborhoods of varying socioeconomic status (SES) in Delhi, India, observed in winter and summer over 2 years. Daily MSW-burning incidents ranged from 24 to 130/km2-day during winter and 5-87/km2-day during summer, with the highest intensity in low SES neighborhoods. Distinct seasonal and diurnal patterns are observed. The daily mass of MSW-burned was also estimated at 90-1170 kg/km2-day and 13-1100 kg/km2-day in highest to low SES neighborhoods, in winter and summer, respectively. The scaled-up estimate of total MSW-burned for Delhi city ranged from 190 to 246 tons/day, about 2%-3% of total generated MSW; morning-burning contributed >65% of the total. MSW composition varied systematically across neighborhoods and season. Agra had much higher MSW-burning (39-202 incidents/km2-day; 672-3485 kg/km2-day) in the summer. The field method thus captures differences in MSW-burning across cities, neighborhoods, diurnally and seasonally, important for more fine grained air pollution modeling, and for tracking/monitoring policy effectiveness on-ground.

Characterization of uncertainty in estimation of methane collection from select U.S. landfills.

Methane is a potent greenhouse gas generated from the anaerobic decomposition of waste in landfills. If captured, methane can be beneficially used to generate electricity. To inventory emissions and assist the landfill industry with energy recovery projects, the U.S. EPA developed the Landfill Gas Emissions Model (LandGEM) that includes two key parameters: the first-order decay rate (k) and methane production potential (L0). By using data from 11 U.S. landfills, Monte Carlo simulations were performed to quantify the effect of uncertainty in gas collection efficiency and municipal solid waste fraction on optimal k values and collectable methane. A dual-phase model and associated parameters were also developed to evaluate its performance relative to a single-phase model (SPM) similar to LandGEM. The SPM is shown to give lower error in estimating methane collection, with site-specific best-fit k values. Most of the optimal k values are notably greater than the U.S. EPA's default of 0.04 yr(-1), which implies that the gas generation decreases more rapidly than predicted at the current default. We translated the uncertainty in collectable methane into uncertainty in engine requirements and potential economic losses to demonstrate the practical significance to landfill operators. The results indicate that landfill operators could overpay for engine capacity by $30,000-780,000 based on overestimates of collectable methane.

How much do electric drive vehicles matter to future U.S. emissions?

Hybrid, plug-in hybrid, and battery electric vehicles--known collectively as electric drive vehicles (EDVs)--may represent a clean and affordable option to meet growing U.S. light duty vehicle (LDV) demand. The goal of this study is 2-fold: identify the conditions under which EDVs achieve high LDV market penetration in the U.S. and quantify the associated change in CO2, SO2, and NOX emissions through midcentury. We employ the Integrated MARKAL-EFOM System (TIMES), a bottom-up energy system model, along with a U.S. data set developed for this analysis. To characterize EDV deployment through 2050, varying assumptions related to crude oil and natural gas prices, a CO2 policy, a federal renewable portfolio standard, and vehicle battery cost were combined to form 108 different scenarios. Across these scenarios, oil prices and battery cost have the biggest effect on EDV deployment. The model results do not demonstrate a clear and consistent trend toward lower system-wide emissions as EDV deployment increases. In addition to the trade-off between lower tailpipe and higher electric sector emissions associated with plug-in vehicles, the scenarios produce system-wide emissions effects that often mask the effect of EDV deployment.

Using observed data to improve estimated methane collection from select U.S. landfills.

The anaerobic decomposition of solid waste in a landfill produces methane, a potent greenhouse gas, and if recovered, a valuable energy commodity. Methane generation from U.S. landfills is usually estimated using the U.S. EPA's Landfill Gas Emissions Model (LandGEM). Default values for the two key parameters within LandGEM, the first-order decay rate (k) and the methane production potential (L0) are based on data collected in the 1990s. In this study, observed methane collection data from 11 U.S. landfills and estimates of gas collection efficiencies developed from site-specific gas well installation data were included in a reformulated LandGEM equation. Formal search techniques were employed to optimize k for each landfill to find the minimum sum of squared errors (SSE) between the LandGEM prediction and the observed collection data. Across nearly all landfills, the optimal k was found to be higher than the default AP-42 of 0.04 yr(-1) and the weighted average decay for the 11 landfills was 0.09 - 0.12 yr(-1). The results suggest that the default k value assumed in LandGEM is likely too low, which implies that more methane is produced in the early years following waste burial when gas collection efficiencies tend to be lower.