. In an attempt to improve the forecasting of atmospheric aerosols, the ensemble square root filter algorithm was extended to simultaneously optimize the chemical initial conditions (ICs) and emission input. The forecast model, which was expanded by combining the Weather Research and Forecasting with Chemistry (WRF-Chem) model and a forecast model of emission scaling factors, generated both chemical concentration fields and emission scaling factors. The forecast model of emission scaling factors was developed by using the ensemble concentration ratios of the WRF-Chem forecast chemical concentrations and also the time smoothing operator. Hourly surface fine particulate matter (PM2. 5) observations were assimilated in this system over China from 5 to 16 October 2014. A series of 48 h forecasts was then carried out with the optimized initial conditions and emissions on each day at 00:00 UTC and a control experiment was performed without data assimilation. In addition, we also performed an experiment of pure assimilation chemical ICs and the corresponding 48 h forecasts experiment for comparison. The results showed that the forecasts with the optimized initial conditions and emissions typically outperformed those from the control experiment. In the Yangtze River delta (YRD) and the Pearl River delta (PRD) regions, large reduction of the root-mean-square errors (RMSEs) was obtained for almost the entire 48 h forecast range attributed to assimilation. In particular, the relative reduction in RMSE due to assimilation was about 37.5 % at nighttime when WRF-Chem performed comparatively worse. In the Beijing–Tianjin–Hebei (JJJ) region, relatively smaller improvements were achieved in the first 24 h forecast but then no improvements were achieved afterwards. Comparing to the forecasts with only the optimized ICs, the forecasts with the joint adjustment were always much better during the night in the PRD and YRD regions. However, they were very similar during daytime in both regions. Also, they performed similarly for almost the entire 48 h forecast range in the JJJ region.

/pdf/improving-pm-2-5-forecast-over-china-by-the-joint-adjustment-1xp4tihq2y.pdf

Improving PM 2. 5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter

. An ensemble Kalman filter data assimilation (DA) system has been
developed to improve air quality forecasts using surface measurements of
PM 10 , PM 2.5 , SO2 , NO2 , O3 , and CO together
with an online regional chemical transport model, WRF-Chem (Weather Research
and Forecasting with Chemistry). This DA system was applied to simultaneously
adjust the chemical initial conditions (ICs) and emission inputs of the
species affecting PM 10 , PM 2.5 , SO2 , NO2 ,
 O3 , and CO concentrations during an extreme haze episode that occurred in early
October 2014 over East Asia. Numerical experimental results indicate that
ICs played key roles in PM 2.5 , PM 10 and CO forecasts during the
severe haze episode over the North China Plain. The 72 h verification
forecasts with the optimized ICs and emissions performed very similarly to
the verification forecasts with only optimized ICs and the prescribed
emissions. For the first-day forecast, near-perfect verification forecasts
results were achieved. However, with longer-range forecasts, the DA impacts
decayed quickly. For the SO2 verification forecasts, it was efficient
to improve the SO2 forecast via the joint adjustment of SO2 
ICs and emissions. Large improvements were achieved for SO2 forecasts
with both the optimized ICs and emissions for the whole 72 h forecast range.
Similar improvements were achieved for SO2 forecasts with optimized
ICs only for the first 3 h, and then the impact of the ICs decayed
quickly. For the NO2 verification forecasts, both forecasts performed
much worse than the control run without DA. Plus, the 72 h O3 
verification forecasts performed worse than the control run during the
daytime, due to the worse performance of the NO2 forecasts, even
though they performed better at night. However, relatively favorable
 NO2 and O3 forecast results were achieved for the Yangtze
River delta and Pearl River delta regions.

/pdf/the-impact-of-multi-species-surface-chemical-observation-1080zun1hp.pdf

The impact of multi-species surface chemical observation assimilation on air quality forecasts in China

A comprehensive investigation using the air quality network and meteorological data of China in 2015 showed that PM2.5 driven by cold surges from the ground level could travel up to 2000 km from northern to southern China within two days. Air pollution is more severe and prominent during the winter in north China due to seasonal variations in energy usage, trade wind movements, and industrial emissions. In February 2015, two cold surges traveling from north China caused a temporary increase in the concentration of PM2.5 in Shanghai. Subsequently, the concentration of PM2.5 in Xiamen increased to a high of 80 µg/m3
 , which is double the average PM2.5 concentration in Xiamen during the winter. This finding is a new long-range transport mechanism comparing to the well-established mechanism, with long-range transport more likely to occur in the upper troposphere than at lower levels. These observations were validated by results from the back trajectory analysis and the RAMS- CMAQ model. While wind speed was found to be a major facilitator in transporting PM2.5 from Beijing to Xiamen, more investigation is required to understand the complex relationship between wind speed and PM2.5 and how it moderates air quality in Beijing, Shanghai, and Xiamen.

/pdf/large-scale-transport-of-pm-2-5-in-the-lower-troposphere-3l3bnxditf.pdf

Large-scale transport of PM 2.5 in the lower troposphere during winter cold surges in China

The crop residue burning in India particularly North-western India is responsible for air pollution episodes and public health concerns; greenhouse gases emissions and radiation imbalance; and declining soil organic matter and soil productivity. The objectives of this paper are to estimate the crop residue burning and emissions from crop residue burning, to recommend interventions in crop residue management and to propose a crop residue management-bioeconomy model incorporating strategies to sustainably manage the crop residues through interventions that enable waste valorization, food and nutritional security, farmers' livelihood and sustainable agricultural production system. A national inventory on crop residue burning including the pollutant species was prepared using the IPCC methodology. The crop types included for the estimation are cereals, pulses, oilseeds, sugarcane, cotton, jute and Mesta. The total amount of crop residues generated and burned for the year 2017-18 was estimated at 516 million tonnes and 116 million tonnes respectively. It is estimated that 116.3 Tg of crop residues burning released about 176.1 Tg of CO2, 10 Tg of CO, 313.9 Gg of CH4, 8.14 Gg of N2O, 151.14 Gg of NH3, 813.8 Gg of NMVOC, 453.4 Gg of PM2.5, and 935.9 Gg of PM10. The emission estimates can be a proxy to prepare the national level inventory of air pollutant species from crop residue burning. The crop residue management (CRM) demands a transition from the traditional zone of CRM to bioeconomy zone of CRM, wherein the interventions aim at the sustainability of agroecosystem. The proposed bioeconomy model has a four-pronged strategy that includes smart agriculture practices, waste bioeconomy involving aspirational principles of bioeconomy, capacity building of stakeholders’ and proactive government policy. Sustainable agricultural bioeconomy provides ample opportunities to reduce crop residue burning, increase farmers’ livelihood and decarbonize the agricultural production. India’s efforts and policies can provide lessons for other agricultural regions having similar environmental constraints.

/pdf/nexus-between-crop-residue-burning-bioeconomy-and-1h6j6qwzb9.pdf

Nexus Between Crop Residue Burning, Bioeconomy and Sustainable Development Goals Over North-Western India

High resolution fossil fuel combustion CO 2 emission fluxes for the United States Kevin R. Gurney 1 , Daniel L. Mendoza 1 , Yuyu Zhou 1 , Marc L. Fischer 2 , Chris C. Miller 1 , Sarath Geethakumar 1 , Stephane de la Rue du Can 2 Department of Earth and Atmospheric Sciences/Department of Agronomy, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907; 2 Atmospheric Science Department, Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, 90K-125, Berkeley, CA 94720 Corresponding Author: Kevin Robert Gurney Department of Earth and Atmospheric Sciences/Department of Agronomy, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907

High resolution fossil fuel combustion CO2 emission fluxes for theUnited States

. In order to optimize surface CO2 fluxes at grid scales, a regional surface CO2 flux inversion system (Carbon Flux Inversion system and Community Multi-scale Air Quality, CFI-CMAQ) has been developed by applying the ensemble Kalman filter (EnKF) to constrain the CO2 concentrations and applying the ensemble Kalman smoother (EnKS) to optimize the surface CO2 fluxes. The smoothing operator is associated with the atmospheric transport model to constitute a persistence dynamical model to forecast the surface CO2 flux scaling factors. In this implementation, the "signal-to-noise" problem can be avoided; plus, any useful observed information achieved by the current assimilation cycle can be transferred into the next assimilation cycle. Thus, the surface CO2 fluxes can be optimized as a whole at the grid scale in CFI-CMAQ. The performance of CFI-CMAQ was quantitatively evaluated through a set of Observing System Simulation Experiments (OSSEs) by assimilating CO2 retrievals from GOSAT (Greenhouse Gases Observing Satellite). The results showed that the CO2 concentration assimilation using EnKF could constrain the CO2 concentration effectively, illustrating that the simultaneous assimilation of CO2 concentrations can provide convincing CO2 initial analysis fields for CO2 flux inversion. In addition, the CO2 flux optimization using EnKS demonstrated that CFI-CMAQ could, in general, reproduce true fluxes at grid scales with acceptable bias. Two further sets of numerical experiments were conducted to investigate the sensitivities of the inflation factor of scaling factors and the smoother window. The results showed that the ability of CFI-CMAQ to optimize CO2 fluxes greatly relied on the choice of the inflation factor. However, the smoother window had a slight influence on the optimized results. CFI-CMAQ performed very well even with a short lag-window (e.g. 3 days).

/pdf/a-regional-carbon-data-assimilation-system-and-its-13heoraj74.pdf

A regional carbon data assimilation system and its preliminary evaluation in East Asia

The burning of crop residues emits large quantities of atmospheric aerosols. Published studies have developed inventories of emissions from crop residue burning based on statistical data. In contrast, this study used satellite-retrieved land-cover data (1 km × 1 km) as activity data to compile an inventory of atmospheric pollutants emitted from the burning of crop residues in China in {dy2015}. The emissions of PM10, PM2.5, VOCs, NOx, SO2, CO, and NH3 from burning crop straw on nonirrigated farmland in China in 2015 were 610.5, 598.4, 584.4, 230.6, 35.4, 3329.3, and 36.1 Gg (1 Gg = 109 g), respectively; the corresponding emissions from burning paddy rice residues were 234.1, 229.7, 342.3, 57.5, 57.5, 1122.1, and 21.5 Gg, respectively. The emissions from crop residue burning showed large spatial and temporal variations. The emissions of particulate matter and gaseous pollutants from crop residue burning in nonirrigated farmland were highest in east China, particularly in Shandong, Henan, Anhui, and Sichuan provinces. Emissions from burning paddy rice residue were highest in east and central China, with particularly high levels in Shandong, Jiangsu, Zhejiang, and Hunan provinces. The monthly variations in atmospheric pollutant emissions were similar among different regions, with the highest levels observed in October in north, northeast, northwest, east, and southwest China and in June and July in central and south China. The developed inventory of emissions from crop residue burning is expected to help improve air quality models by providing high-resolution spatial and temporal data.

Mengduo Zhang

Papers

A regional carbon data assimilation system and its preliminary evaluation in East Asia

Inventory of Atmospheric Pollutant Emissions from Burning of Crop Residues in China Based on Satellite-retrieved Farmland Data