@article{aneja_schlesinger_li_nahas_battye_2020, title={Characterization of the Global Sources of Atmospheric Ammonia from Agricultural Soils}, volume={125}, ISSN={["2169-8996"]}, url={https://doi.org/10.1029/2019JD031684}, DOI={10.1029/2019JD031684}, abstractNote={Abstract Global ammonia (NH 3 ) emissions to the atmosphere are projected to increase in the coming years with the increased use of synthetic nitrogen fertilizers and cultivation of nitrogen‐fixing crops. A statistical model (NH 3 _STAT) is developed for characterizing atmospheric NH 3 emissions from agricultural soils and compared to the performance of other global and regional NH 3 models (e.g., Emission Database for Global Atmospheric Research, Magnitude and Seasonality of Agricultural Emissions, MIX, and U.S. Environmental Protection Agency). The statistical model was developed from a multiple linear regression between NH 3 emission and the physicochemical variables. The model was evaluated for 2012 NH 3 emissions. The results indicate that, in comparison to other data sets, the model provides a lower global NH 3 estimate by 58%, (NH 3 _STAT: 13.9 Tg N yr −1 ; Emission Database for Global Atmospheric Research: 33.0 Tg N yr −1 ). We also performed a region‐based analysis (United States, India, and China) using the NH 3 _STAT model. For the United States, our model produces an estimate that is a ~1.4 times higher in comparison to the Environmental Protection Agency. Meanwhile, the NH 3 _STAT estimate for India shows NH 3 emissions between 0.8 and 1.4 times lower when compared to other data sets. A lower estimate is also seen for China, where the model estimates NH 3 emissions 0.4 to 5 times lower than other data sets. The difference in the global estimates is attributed to the lower estimates in major agricultural countries like China and India. The statistical model captures the spatial distribution of global NH 3 emissions by utilizing a simplified approach compared to other readily available data sets. Moreover, the NH 3 _STAT model provides an opportunity to predict future NH 3 emissions in a changing world.}, number={3}, journal={JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES}, publisher={American Geophysical Union (AGU)}, author={Aneja, Viney P. and Schlesinger, William H. and Li, Qi and Nahas, Alberth and Battye, William H.}, year={2020}, month={Feb} }
 @article{aneja_schlesinger_li_nahas_battye_2019, title={Characterization of atmospheric nitrous oxide emissions from global agricultural soils}, volume={1}, ISBN={2523-3971}, url={https://doi.org/10.1007/s42452-019-1688-5}, DOI={10.1007/s42452-019-1688-5}, abstractNote={Nitrous oxide (N2O) is a potent greenhouse gas with an atmospheric lifetime of ~ 114 years. Agriculture activities are the main sources for N2O emission into the atmosphere by human activities. Global N2O emissions into the atmosphere are projected to increase in the coming years as demand for food, fibre and energy increases owing to increasing global population. Here, a statistical model (N2O_STAT) is developed for characterizing atmospheric N2O emissions from agricultural sources. We obtained N2O emissions and physicochemical variables (i.e. air temperature, soil temperature, soil moisture, soil pH, and N input to the soil) from published journal articles since 2000. A statistical model was developed by expressing a multiple linear regression equation between N2O emission and the physicochemical variables. The model was evaluated for 2012 N2O emissions. Results of the model are compared with other global and regional N2O models (e.g. EDGAR, EPA/USGS, and FAOSTAT). In comparison with other data sets, the model generates a lower global N2O estimate by 9–20% (N2O_STAT: 3.75 Tg N yr−1; EDGAR: 4.49 Tg N yr−1; FAO: 4.07 Tg N yr−1), but is ~ 25% higher when compared to Bouwman et al. (Glob Biogeochem Cycles 16:1–9. https://doi.org/10.1029/2001gb001812
 , 2002) (2.80 Tg N yr−1). We also performed a region-based analysis (USA, India, and China) using the N2O_STAT model. For the USA, our model produces an estimate that ranges from − 13 to + 32% in comparison with other published data sets. Meanwhile, the N2O_STAT model estimate for India shows N2O emissions between − 56 and + 14% when compared to other data sets. A much lower estimate is seen for China, where the model estimates N2O emissions 38–177% lower than other data sets. The N2O_STAT model provides an opportunity to predict future N2O emissions in a changing world.}, number={12}, journal={SN APPLIED SCIENCES}, publisher={Springer Science and Business Media LLC}, author={Aneja, Viney P. and Schlesinger, William H. and Li, Qi and Nahas, Alberth and Battye, William H.}, year={2019}, month={Dec} }
 @article{zhang_hong_yahya_li_zhang_he_2016, title={Comprehensive evaluation of multi-year real-time air quality forecasting using an online-coupled meteorology-chemistry model over southeastern United States}, volume={138}, ISSN={["1873-2844"]}, DOI={10.1016/j.atmosenv.2016.05.006}, abstractNote={An online-coupled meteorology-chemistry model, WRF/Chem-MADRID, has been deployed for real time air quality forecast (RT-AQF) in southeastern U.S. since 2009. A comprehensive evaluation of multi-year RT-AQF shows overall good performance for temperature and relative humidity at 2-m (T2, RH2), downward surface shortwave radiation (SWDOWN) and longwave radiation (LWDOWN), and cloud fraction (CF), ozone (O3) and fine particles (PM2.5) at surface, tropospheric ozone residuals (TOR) in O3 seasons (May-September), and column NO2 in winters (December-February). Moderate-to-large biases exist in wind speed at 10-m (WS10), precipitation (Precip), cloud optical depth (COT), ammonium (NH4+), sulfate (SO42−), and nitrate (NO3−) from the IMPROVE and SEARCH networks, organic carbon (OC) at IMPROVE, and elemental carbon (EC) and OC at SEARCH, aerosol optical depth (AOD) and column carbon monoxide (CO), sulfur dioxide (SO2), and formaldehyde (HCHO) in both O3 and winter seasons, column nitrogen dioxide (NO2) in O3 seasons, and TOR in winters. These biases indicate uncertainties in the boundary layer and cloud process treatments (e.g., surface roughness, microphysics cumulus parameterization), emissions (e.g., O3 and PM precursors, biogenic, mobile, and wildfire emissions), upper boundary conditions for all major gases and PM2.5 species, and chemistry and aerosol treatments (e.g., winter photochemistry, aerosol thermodynamics). The model shows overall good skills in reproducing the observed multi-year trends and inter-seasonal variability in meteorological and radiative variables such as T2, WS10, Precip, SWDOWN, and LWDOWN, and relatively well in reproducing the observed trends in surface O3 and PM2.5, but relatively poor in reproducing the observed column abundances of CO, NO2, SO2, HCHO, TOR, and AOD. The sensitivity simulations using satellite-constrained boundary conditions for O3 and CO show substantial improvement for both spatial distribution and domain-mean performance statistics. The model’s forecasting skills for air quality can be further enhanced through improving model inputs (e.g., anthropogenic emissions for urban areas and upper boundary conditions of chemical species), meteorological forecasts (e.g., WS10, Precip) and meteorologically-dependent emissions (e.g., biogenic and wildfire emissions), and model physics and chemical treatments (e.g., gas-phase chemistry in winter conditions, cloud processes and their interactions with radiation and aerosol).}, journal={ATMOSPHERIC ENVIRONMENT}, author={Zhang, Yang and Hong, Chaopeng and Yahya, Khairunnisa and Li, Qi and Zhang, Qiang and He, Kebin}, year={2016}, month={Aug}, pages={162–182} }