1,4-Dioxane (dioxane) is an emerging groundwater contaminant that has significant regulatory implications and potential remediation costs, but our current understanding of its occurrence and behavior is limited. This study used intensive data mining to identify and evaluate >2000 sites in California where groundwater has been impacted by chlorinated solvents and/or dioxane. Dioxane was detected at 194 of these sites, with 95% containing one or more chlorinated solvents. Dioxane frequently co-occurs with 1,1,1-trichloroethene (1,1,1-TCA) (76% of the study sites), but despite this, no dioxane analyses were conducted at 332 (67%) of the sites where 1,1,1-TCA was detected. At sites where dioxane has been identified, plumes are dilute but not large (median maximal concentration of 365 μg/L; median plume length of 269 m) and have been delineated to a similar extent as typically co-occurring chlorinated solvents. Furthermore, at sites where dioxane and chlorinated solvents co-occur, dioxane plumes are frequently shorter than the chlorinated solvent plumes (62%). The results suggest that dioxane has not migrated beyond chlorinated solvent plumes and existing monitoring networks at the majority of sites, and that the primary risk is the large number of sites where dioxane is likely to be present but has yet to be identified.

We perform a state-specific life-cycle assessment of greenhouse gases (GHG) (CO2eq) and sulfur dioxide (SO2) emissions in India for representative passenger vehicles (two-wheelers, three-wheelers, four-wheelers, and buses) and technologies (internal combustion engine, battery electric, hybrid electric, and plug-in hybrid electric vehicles). We find that in most states, four-wheeler battery-electric vehicles (BEVs) have higher GHG and SO2 emissions than other conventional or alternative vehicles. Electrification of those vehicle classes under present conditions would not lead to emission reductions. Electrified buses and three-wheelers are the best strategies to reduce GHG emissions in many states, but they are also the worst strategy in terms of SO2 emissions. Electrified two-wheelers have lower SO2 emissions than gasoline in one state. The Indian grid would need to decrease its carbon dioxide emissions by 38–52% and SO2 emissions by 58–97% (depending on the state) for widespread vehicle electrification for sustainability purposes to make sense. If the 2030 goals for India under the Glasgow COP are met, we find that four-wheeler BEVs still have higher GHG emissions in 18 states compared to a conventional gasoline compact four wheeler, and all states will have higher SO2 emissions for BEVs across all vehicle types compared to their conventional counterparts.

Urban BG Near local Rd Near local Rd + restaurant Near arterial Rd Urban st canyon Near highway Near industry Figure S1: Study area map and measurements sites. Measurements of particle number and PM 2.5 mass concentrations are collected during winter of 2017 and 2018. A circle symbol 101 (D-F) LUR predictions, and (G-I) 1-km CTM predictions. In panels C, F, and I , PNC to PM 2.5 102 mass slopes for the various sources (e.g., natural gas, gasoline) are shown from literature 1-5 ; 103 the red lines in these panels are the linear fit of the data/predictions.

Emission factors from Indian electricity remain poorly characterized, despite known spatial and temporal variability. Limited publicly available emissions and generation data at sufficient detail make it difficult to understand the consequences of emissions to climate change and air pollution, potentially missing cost-effective policy designs for the world’s third largest power grid. We use reduced-form and full-form power dispatch models to quantify current (2017–2018) and future (2030–2031) marginal CO2, SO2, NOX, and PM2.5 emission factors from Indian power generation. These marginal emissions represent emissions changes due to small changes in demand. For 2017–2018, spatial variability in marginal CO2 emission factors range 3 orders of magnitude across India’s states. There is limited seasonal and intraday variability with coal generation likely to meet changes in demand more than half the time in more than half of the states. Assuming the Government of India approximate 2030 targets, the median marginal CO2 emission factor across states decreases by approximately a factor of 2, but emission factors still span 3 orders of magnitude across states. Under 2030–2031 assumptions there is greater seasonal and intraday variability by up to factors of two and four, respectively. Estimates provide emission factors to evaluate interventions such as electric vehicles, increased air conditioning, and energy efficiency.

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