[i] An inventory of air pollutant emissions in Asia in the year 2000 is developed to support atmospheric modeling and analysis of observations taken during the TRACE-P experiment funded by the National Aeronautics and Space Administration (NASA) and the ACE-Asia experiment funded by the National Science Foundation (NSF) and the National Oceanic and Atmospheric Administration (NOAA). Emissions are estimated for all major anthropogenic sources, including biomass burning, in 64 regions of Asia. We estimate total Asian emissions as follows: 34.3 Tg SO 2 , 26.8 Tg NO x , 9870 Tg CO 2 , 279 Tg CO, 107 Tg CH 4 , 52.2 Tg NMVOC, 2.54 Tg black carbon (BC), 10.4 Tg organic carbon (OC), and 27.5 Tg NH 3 . In addition, NMVOC are speciated into 19 subcategories according to functional groups and reactivity. Thus we are able to identify the major source regions and types for many of the significant gaseous and particle emissions that influence pollutant concentrations in the vicinity of the TRACE-P and ACE-Asia field measurements. Emissions in China dominate the signature of pollutant concentrations in this region, so special emphasis has been placed on the development of emission estimates for China. China's emissions are determined to be as follows: 20.4 Tg SO 2 , 11.4 Tg NO x , 3820 Tg CO 2 , 116 Tg CO, 38.4 Tg CH 4 , 17.4 Tg NMVOC, 1.05 Tg BC, 3.4 Tg OC, and 13.6 Tg NH 3 . Emissions are gridded at a variety of spatial resolutions from 1° × 1° to 30 s x 30 s, using the exact locations of large point sources and surrogate GIS distributions of urban and rural population, road networks, landcover, ship lanes, etc. The gridded emission estimates have been used as inputs to atmospheric simulation models and have proven to be generally robust in comparison with field observations, though there is reason to think that emissions of CO and possibly BC may be underestimated. Monthly emission estimates for China are developed for each species to aid TRACE-P and ACE-Asia data interpretation. During the observation period of March/ April, emissions are roughly at their average values (one twelfth of annual). Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of ±16% for SO 2 to a high of ±450% for OC.

An inventory of gaseous and primary aerosol emissions in Asia in the year 2000 : NASA global tropospheric experiment transport and chemical evolution over the pacific (TRACE-P): Measurements and analysis (TRACEP1)

Air pollution scenario over Pakistan: characterization and ranking of extremely polluted cities using long-term concentrations of aerosols and trace gases

Reanalysis precipitation products (RPPs) are frequently used for studying the water cycle changes from short to long-term scale globally. In the current study, ERA-5 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japanese 55-year Reanalysis (JRA-55), the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), and the Climate Forecast System version 2 (CFS-2) precipitation products were evaluated with the rain-gauge data as a reference during 1981–2019 over Pakistan. The performance was assessed using statistical error metrics on daily, monthly, and annual timescales. The reanalysis precipitation products (RPPs) captured the precipitation intensities and the extreme precipitation events (75th to 99th percentile) across climatic classes. On a daily scale, the ERA-5 follows rain-gauges very closely (RC: 0.67, R: 0.81, RMSE: 1.69 mm), consistently capturing the precipitation intensities (light to violent) and extreme precipitation events (95th percentile), followed by CFS-2. The MERRA-2 captured precipitation intensity but did not detect extreme precipitation events in some regions. The JRA-55 produced good results in the central area while overestimated the precipitation in the northern and southern parts of the study area. On a monthly time scale, ERA-5 performed well as compared to the rest of RPPs, with regression coefficient values of 0.91, correlation coefficient (0.96), and a lower value of RMSE (11.09 mm), followed by JRA-55, MERRA-2, and CFS-2. All the RPPs performed better in winter, pre-monsoon, and post-monsoon seasons with slight deviations/differences, but in monsoon season, the ERA-5 and JRA-55 (MERRA-2, CFS-2) overestimated (underestimated) precipitation mean. The findings can help the researchers select reliable datasets for bias correction of the projections and real-time application in flood, drought estimation, and prediction.

Performance evaluation of ERA-5, JRA-55, MERRA-2, and CFS-2 reanalysis datasets, over diverse climate regions of Pakistan

The substantial reliance of South Asia (SA) to rain-based agriculture makes the region susceptible to food scarcity due to droughts. Previously, most research on SA has emphasized the meteorological aspects with little consideration of agrarian drought impressions. The insufficient amount of in situ precipitation data across SA has also hindered thorough investigation in the agriculture sector. In recent times, models, satellite remote sensing, and reanalysis products have increased the amount of data. Hence, soil moisture, precipitation, terrestrial water storage (TWS), and vegetation condition index (VCI) products have been employed to illustrate SA droughts from 1982 to 2019 using a standardized index/anomaly approach. Besides, the relationships of these products towards crop production are evaluated using the annual national production of barley, maize, rice, and wheat by computing the yield anomaly index (YAI). Our findings indicate that MERRA-2, CPC, FLDAS (soil moisture), GPCC, and CHIRPS (precipitation) are alike and constant over the entire four regions of South Asia (northwest, southwest, northeast, and southeast). On the other hand, GLDAS and ERA5 remain poor when compared to other soil moisture products and identified drought conditions in regions one (northwest) and three (northeast). Likewise, TWS products such as MERRA-2 TWS and GRACE TWS (2002–2014) followed the patterns of ERA5 and GLDAS and presented divergent and inconsistent drought patterns. Furthermore, the vegetation condition index (VCI) remained less responsive in regions three (northeast) and four (southeast) only. Based on annual crop production data, MERRA-2, CPC, FLDAS, GPCC, and CHIRPS performed fairly well and indicated stronger and more significant associations (0.80 to 0.96) when compared to others. Thus, the current outcomes are imperative for gauging the deficient amount of data in the SA region, as they provide substitutes for agricultural drought monitoring.

Comparison of Multi-Year Reanalysis, Models, and Satellite Remote Sensing Products for Agricultural Drought Monitoring over South Asian Countries

Classification of aerosols over Saudi Arabia from 2004–2016

Drought is an intricate atmospheric phenomenon with the greatest impacts on food security and agriculture in South Asia. Timely and appropriate forecasting of drought is vital in reducing its negative impacts. This study intended to explore the performance of the evaporative stress index (ESI), vegetation health index (VHI), enhanced vegetation index (EVI), and standardized anomaly index (SAI) based on satellite remote sensing data from 2002–2019 for agricultural drought assessment in Afghanistan, Pakistan, India, and Bangladesh. The spatial maps were generated against each index, which indicated a severe agricultural drought during the year 2002, compared to the other years. The results showed that the southeast region of Pakistan, and the north, northwest, and southwest regions of India and Afghanistan were significantly affected by drought. However, Bangladesh faced substantial drought in the northeast and northwest regions during the drought year (2002). The longest drought period of seven months was observed in India followed by Pakistan and Afghanistan with six months, while, only three months were perceived in Bangladesh. The correlation between drought indices and climate variables such as soil moisture has remained a significant drought-initiating variable. Furthermore, this study confirmed that the evaporative stress index (ESI) is a good agricultural drought indicator, being quick and with greater sensitivity, and thus advantageous compared to the VHI, EVI, and SAI vegetation indices.

https://www.mdpi.com/2072-4292/13/11/2059/pdf

Remote Sensing Indices for Spatial Monitoring of Agricultural Drought in South Asian Countries

In this study, MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) level-2 Dark Target (DT) Aerosol Optical Depth (AOD) observations at 550 nm (AOD550) for the highest quality flag assurance (QA = 3) were obtained to analyze spatiotemporal variations of aerosol optical properties over the Yellow and the Bohai Sea from 2002 to 2017. Spectral AOD observations at 470 nm (AOD470) and 660 nm (AOD660) were obtained to calculate Angstrom Exponent (AE470–660) and classify the aerosol types including clean continental (CC), clean maritime (CM) biomass and urban industrial (BUI), dust (D), and mixed (MXD) aerosol types. Results showed a very distinct spatial pattern of AOD distribution over the Bohai Sea which looks suspicious, i.e., high aerosol loadings (AOD > 0.8) throughout the entire time period, whereas relative low AOD distribution was observed over the adjacent land pixels especially in autumn and winter, which suggested that the DT algorithm might be influenced by a large number of sediments located in the Bohai Sea. Significant differences in spatial distributions were found in different seasons in terms of area coverage as a maximum number of pixels were available during autumn, and regional high and low aerosol loadings were observed during autumn and summer, respectively. Trend analysis from 2002 to 2017 showed that AOD was increased up to 0.04 over the Bohai Sea and decreased up to 0.04 over the Yellow Sea, and this trend varies from month to month. Aerosol classification showed significant contributions of BUI and CC over the region, and contributions of CM, DUST, and MXD aerosols over the Yellow Sea were relatively high compared to the Bohai Sea.

Long-term spatiotemporal variations of aerosol optical depth over Yellow and Bohai Sea.

In this study, MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) level-2 Dark Target (DT) Aerosol Optical Depth (AOD) products at 3 km (DT3K) and 10 km (DT10K) spatial resolutions were validated over the China seas and the eastern Indian Ocean against Maritime Aerosol Network (MAN) Level 1.5 AOD measurements collected through 13 cruises from 2010 to 2014. For this, DT3K and DT10K AOD observations were obtained from four Scientific Data Sets (SDS), i.e., “Effective_Optical_Depth_Average_Ocean” (EODAOAOD), “Effective_Optical_Depth_Best_Ocean” (EODBOAOD), “Image_Optical_Depth_Land_And_Ocean” (IODLAOAOD) and “Optical_Depth_Land_And_Ocean” (ODLAOAOD). The MAN AOD measurements were filtered within (i) ±2 h, (ii) ±4 h, (iii) ±6 h, and (iv) ±12 h of MODIS overpass time. Results showed that the DT10K and DT3K performed equally over the China seas and the eastern Indian Ocean in terms of retrievals quality and agreement with the MAN AOD measurements, whereas the DT3K has less coincident observations than the DT10K. For seasonal analysis, larger underestimation in the DT algorithm was observed in autumn followed by spring, whereas retrievals were well correlated with the MAN AOD data in summer. Overall, this study found that ODLAOAOD observations for the DT3K and DT10K were much better than EODAOAOD, EODBOAOD and IODLAOAOD in terms of high correlation and a large percentage of the AOD retrievals within the Expected Error (EE = +(0.04 + 10%), −(0.02 + 10%)).

Weijun Zhu

Papers

Comparison of Multi-Year Reanalysis, Models, and Satellite Remote Sensing Products for Agricultural Drought Monitoring over South Asian Countries

Remote Sensing Indices for Spatial Monitoring of Agricultural Drought in South Asian Countries

Long-term spatiotemporal variations of aerosol optical depth over Yellow and Bohai Sea.

Validation of MODIS C6 Dark Target Aerosol Products at 3 km and 10 km Spatial Resolutions Over the China Seas and the Eastern Indian Ocean