@article{bray_nahas_battye_aneja_2021, title={Impact of lockdown during the COVID-19 outbreak on multi-scale air quality}, volume={254}, ISSN={["1873-2844"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85104410171&partnerID=MN8TOARS}, DOI={10.1016/j.atmosenv.2021.118386}, abstractNote={One of the multi-facet impacts of lockdowns during the unprecedented COVID-19 pandemic was restricted economic and transport activities. This has resulted in the reduction of air pollution concentrations observed globally. This study is aimed at examining the concentration changes in air pollutants (i.e., carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and particulate matters (PM2.5 and PM10) during the period March-April 2020. Data from both satellite observations (for NO2) and ground-based measurements (for all other pollutants) were utilized to analyze the changes when compared against the same months between 2015 and 2019. Globally, space borne NO2 column observations observed by satellite (OMI on Aura) were reduced by approximately 9.19% and 9.57%, in March and April 2020, respectively because of public health measures enforced to contain the coronavirus disease outbreak (COVID-19). On a regional scale and after accounting for the effects of meteorological variability, most monitoring sites in Europe, USA, China, and India showed declines in CO, NO2, SO2, PM2.5, and PM10 concentrations during the period of analysis. An increase in O3 concentrations occurred during the same period. Meanwhile, four major cities case studies i.e. in New York City (USA), Milan (Italy), Wuhan (China), and New Delhi (India) have also shown a similar reduction trends as observed on the regional scale, and an increase in ozone concentration. This study highlights that the reductions in air pollutant concentrations have overall improved global air quality likely driven in part by economic slowdowns resulting from the global pandemic.}, journal={ATMOSPHERIC ENVIRONMENT}, author={Bray, Casey D. and Nahas, Alberth and Battye, William H. and Aneja, Viney P.}, year={2021}, month={Jun} }
 @article{yudistira_sumitro_nahas_riama_2021, title={Learning where to look for COVID-19 growth: Multivariate analysis of COVID-19 cases over time using explainable convolution-LSTM}, volume={109}, ISSN={["1872-9681"]}, DOI={10.1016/j.asoc.2021.107469}, abstractNote={Determinant factors which contribute to the prediction should take into account multivariate analysis for capturing coarse-to-fine contextual information. From the preliminary descriptive analysis, it shows that environmental factor such as UV (ultraviolet) is one of the essential factors that should be considered to observe the COVID-19 epidemic drivers. Moreover, there are education, government, morphological, health, economic, and behavioral factors contributing to the growth of COVID-19. Besides descriptive analysis, in this research, multivariate analysis is considered to provide comprehensive explanations about factors contributing to pandemic dynamics. To achieve rich explanations, visual attribution of explainable Convolution-LSTM is utilized to see high contributing factors responsible for the growth of daily COVID-19 cases. Our model consists of 1 D CNN in the first layer to capture local relationships among variables followed by LSTM layers to capture local dependencies over time. It produces the lowest prediction errors compared to the other existing models. This permits us to employ gradient-based visual attribution for generating saliency maps for each time dimension and variable. These are then used for explaining which variables throughout which period of the interval is contributing for a given time-series prediction, likewise as explaining that during that time intervals were the joint contribution of most vital variables for that prediction. The explanations are useful for stakeholders to make decisions during and post pandemics. The explainable Convolution–LSTMcode is available here: https://github.com/cbasemaster/time-series-attribution.}, journal={APPLIED SOFT COMPUTING}, author={Yudistira, Novanto and Sumitro, Sutiman Bambang and Nahas, Alberth and Riama, Nelly Florida}, year={2021}, month={Sep} }
 @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}, 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} }