Optimized environmental justice calculations for air pollution disparities in Southern California.

An Environmental Justice (EJ) analysis was carried out using full Chemical Transport Models (CTMs) over Los Angeles, California, to determine how the combination of domain size and spatial resolution affects predicted air pollution disparities in present day and future simulations when data support from measurements is not available. One set of simulations used the Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF/Chem) with spatial resolution ranging from 250 m to 36 km, comparable to census tract sizes, over domains ranging in size from 320 km2 to 10,000 km2. A second set of simulations used the UCD/CIT CTM with spatial resolution ranging from 4 km to 24 km over domains ranging in size from 98,000 km2 to 1,000,000 km2. Overall WRF/Chem model accuracy improved approximately 9% as spatial resolution increased from 4 km to 250 m in present-day simulations, with similar results expected for future simulations. Exposure disparity results are consistent with previous findings: the average Non-Hispanic White person in the study domain experiences PM2.5 mass concentrations 6-14% lower than the average resident, while the average Black and African American person experiences PM2.5 mass concentrations that are 3-22% higher than the average resident. Predicted exposure disparities were a function of the model configuration. Increasing the spatial resolution finer than approximately 1 km produced diminishing returns because the increased spatial resolution came at the expense of reduced domain size in order to maintain reasonable computational burden. Increasing domain size to capture regional trends, such as wealthier populations living in coastal areas, identified larger exposure disparities but the benefits were limited. CTM configurations that use spatial resolution/domain size of 1 km/103 km2 and 4 km/104 km2 over Los Angeles can detect a 0.5 µg m-3 exposure difference with statistical power greater than 90%. These configurations represent a balanced approach between statistical power, sensitivity across socio-economic groups, and computational burden when predicting current and future air pollution exposure disparities in Los Angeles.

Adoption of low-carbon fuels reduces race/ethnicity disparities in air pollution exposure in California.

An environmental justice (EJ) analysis shows that adoption of low-carbon energy sources in the year 2050 reduces the race/ethnicity disparity in air pollution exposure in California by as much as 20% for PM2.5 mass and by as much as 40% for PM0.1 mass. An ensemble of six different energy scenarios constructed using the energy-economic optimization model CA-TIMES were evaluated in future years. Criteria pollutant emissions were developed for each energy scenario using the CA-REMARQUE model using 4 km spatial resolution over four major geographic areas in California: the greater San Francisco Bay Area including Sacramento (SFBA&SAC), the San Joaquin Valley (SJV), Los Angeles (LA), and San Diego (SD). The Weather Research & Forecasting (WRF) model was used to predict future meteorology fields by downscaling two different climate scenario (RCP4.5 and RCP8.5) generated by two different GCMs (the Community Climate System Model and the Canadian Earth Systems Model). Simulations were performed over 32 weeks randomly selected during the 10 year window from the year 2046 to 2055 to build up a long-term average in the presence of ENSO variability. The trends associated with low-carbon energy adoption were relatively stable across the ensemble of locations and scenarios. Deeper reductions in the carbon intensity of energy sources progressively reduced exposure to PM2.5 mass and PM0.1 mass for all California residents. The greater adoption of low-carbon fuels also reduced the racial disparity in the PM exposure. The three energy scenarios that achieved an ~80% reduction in GHG emissions relative to 1990 levels simultaneously produced the greatest reduction in PM exposure for all California residents and the greatest reduction in the racial disparity of that exposure. These findings suggest that the adoption of low-carbon energy can improve public health and reduce racial disparities through an improvement in air quality.

Intercomparison of Aerosol Optical Thickness Derived from MODIS and in Situ Ground Datasets over Jaipur, a Semi-arid Zone in India.

The first detailed seasonal validation has been carried out for the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua satellites Level 2.0 Collection Version 5.1 AOT (τMODIS) with Aerosol Robotic Network (AERONET) Level 2.0 AOT (τAERONET) for the years 2009-2012 over semi-arid region Jaipur, northwestern India. The correlation between τMODIS versus τAERONET at 550 nm is determined with different spatial and temporal size windows. The τMODIS overestimates τAERONET within a range of +0.06 ± 0.24 during the pre-monsoon (April-June) season, while it underestimates the τAERONET with -0.04 ± 0.12 and -0.05 ± 0.18 during dry (December-March) and post-monsoon (October-November) seasons, respectively. Correlation without (with) error envelope has been found for pre-monsoon at 0.71 (0.89), post-monsoon at 0.76 (0.94), and dry season at 0.78 (0.95). τMODIS is compared to τAERONET at three more ground AERONET stations in India, i.e., Kanpur, Gual Pahari, and Pune. Furthermore, the performance of MODIS Deep Blue and Aqua AOT550 nm (τDB550 nm and τAqua550 nm) with τAERONET is also evaluated for all considered sites over India along with a U.S. desert site at White Sand, Tularosa Basin, NM. The statistical results reveal that τAqua550 nm performs better over Kanpur and Pune, whereas τDB550 nm performs better over Jaipur, Gual Pahari, and White Sand High Energy Laser Systems Test Facility (HELSTF) (U.S. site).

Vehicular emission inventory of criteria pollutants in Delhi.

The rapid urbanization in Delhi has resulted in a tremendous increase in the number of motor vehicles with the increase in population and urban mobilization. The vehicular traffic is now recognized as one of the main sources of air pollution in Delhi and has noticeable impact on air quality. The emission of criteria pollutants namely Carbon Monoxide (CO), Nitrogen Oxide (NOx) and Particulate Matter (PM) due to vehicles is estimated through the International Vehicle Emission (IVE) model, which includes the different driving modes of vehicles and meteorological parameters. The estimated emissions of Carbon Monoxide (CO), Nitrogen Oxides (NOx) and Particulate Matter (PM) due to different types of vehicles in the year 2008-09 are found to be 509, 194 and 15 tons/day respectively. The diurnal variation of emissions of air pollutants shows two peaks, which are fortunately matching with the morning and evening office hours. The emissions of CO and NOx due to personal cars (PCs) are found to be about 34% and 50% respectively, and the emission of CO due to 2 W (2- Wheeler) is about 61%. Similarly, the Heavy Commercial Vehicles (HCVs) are contributing PM about 92%. The analysis of fuel-wise emission of pollutants reveals that CO is mainly contributed by petrol, and NOx and PM are contributed by diesel. It is also noticeable that CO, NOx and PM emissions at ITO, one of the busiest traffic intersections of Delhi, are approximately 15, 6 and 0.5 tons/day respectively, which are found to be the maximum followed by Kashmiri Gate (ISBT), Nizamuddin etc. The present vehicular emissions inventory has been compared quantitatively with previous studies of Delhi. The present vehicular emission inventory has also validated using US environmental protection agency's (USEPA's) AERMOD model with observed concentration at different locations in Delhi. However, the present study shows that the air quality of Delhi has been degraded due to high level emissions of criteria pollutants.

Forecasting of daily air quality index in Delhi.

As the impact of air pollutants on human health through ambient air address much attention in recent years, the air quality forecasting in terms of air pollution parameters becomes an important topic in environmental science. The Air Quality Index (AQI) can be estimated through a formula, based on comprehensive assessment of concentration of air pollutants, which can be used by government agencies to characterize the status of air quality at a given location. The present study aims to develop forecasting model for predicting daily AQI, which can be used as a basis of decision making processes. Firstly, the AQI has been estimated through a method used by US Environmental Protection Agency (USEPA) for different criteria pollutants as Respirable Suspended Particulate Matter (RSPM), Sulfur dioxide (SO2), Nitrogen dioxide (NO2) and Suspended Particulate Matter (SPM). However, the sub-index and breakpoint concentrations in the formula are made according to Indian National Ambient Air Quality Standard. Secondly, the daily AQI for each season is forecasted through three statistical models namely time series auto regressive integrated moving average (ARIMA) (model 1), principal component regression (PCR) (model 2) and combination of both (model 3) in Delhi. The performance of all three models are evaluated with the help of observed concentrations of pollutants, which reflects that model 3 agrees well with observed values, as compared to the values of model 1 and model 2. The same is supported by the statistical parameters also. The significance of meteorological parameters of model 3 has been assessed through principal component analysis (PCA), which indicates that daily rainfall, station level pressure, daily mean temperature, wind direction index are maximum explained in summer, monsoon, post-monsoon and winter respectively. Further, the variation of AQI during the weekends (holidays) and weekdays are found negligible. Therefore all the days of week are accounted same in the models.