Amir Hossein Souri

Remote sensing evidence of decadal changes in major tropospheric ozone precursors over East Asia

Recent regulatory policies in East Asia reduce ozone precursors, but these changes are spatially and temporally nonuniform. This study investigates variations in the long-term trends of tropospheric NO2, HCHO, and HCHO/NO2 ratios to diagnose ozone sensitivity to changes in NOx and volatile organic compound using the Ozone Monitoring Instrument (OMI). Using an adaptive-degree polynomial filter, we identify extremums of time series of NO2 to determine when and how NO2 change. Due to the regulations in China, trends which were predominantly upward turned downward. The years undergoing these changes primarily happened in 2011 and 2012. OMI column densities, however, suggest that NOx sources in South Korea, the Pearl River Delta (PRD), Taiwan, and Japan have not consistently decreased. Specifically, as Chinese exports of NO2 started subsiding, increasing trends in NO2 columns over several Korean cities, including Seoul, become evident. To quantify the changes in NOx emissions from summertime 2010 to 2014, we conduct a 3D-Var inverse modeling using a regional model with MIX-Asia inventory and estimate NOx emissions (in 2010 and 2014) for the PRD (1.6 and 1.5 Gg/d), the Yangtze River Delta (3.9 and 3.0 Gg/d), north China (15.6 and 14.3 Gg/d), South Korea (1.6 and 1.5 Gg/d), and Japan (2.7 and 2.6 Gg/d). OMI HCHO shows upward trends in East Asia resulting from anthropogenic effects; however, the magnitudes are negative in the PRD, Japan, North Korea, and Taiwan. OMI HCHO/NO2 ratios reveal that while South Korea, Japan, and the south of China have undergone toward more NOx-sensitive regime, areas around the Bohai Sea have become more NOx saturated.

Identification of chemical fingerprints in long-range transport of burning induced upper tropospheric ozone from Colorado to the North Atlantic Ocean

This study investigates a significant biomass burning (BB) event occurred in Colorado of the United States in 2012 using the Community Multi-scale Air Quality (CMAQ) model. The simulation reasonably reproduced the significantly high upper tropospheric O3 concentrations (up to 145ppb) caused by BB emissions. We find the BB-induced O3 was primarily affected by chemical reactions and dispersion during its transport. In the early period of transport, high NOx and VOCs emissions caused O3 production due to reactions with the peroxide and hydroxyl radicals, HO2 and OH. Here, NOx played a key role in O3 formation in the BB plume. The results indicated that HO2 in the BB plume primarily came from formaldehyde (HCHO+hv=2HO2+CO), a secondary alkoxy radical (ROR=HO2). CO played an important role in the production of recycled HO2 (OH+CO=HO2) because of its abundance in the BB plume. The chemically produced HO2 was largely converted to OH by the reactions with NO (HO2+NO=OH+NO2) from BB emissions. This is in contrast to the surface, where HO2 and OH are strongly affected by VOC and HONO, respectively. In the late stages of transport, the O3 concentration was primarily controlled by dispersion. It stayed longer in the upper troposphere compared to the surface due to sustained depletion of NOx. Sensitivity analysis results support that O3 in the BB plume is significantly more sensitive to NOx than VOCs.

Dust storm detection using random forests and physical-based approaches over the Middle East

Dust storms are important phenomena over large regions of the arid and semi-arid areas of the Middle East. Due to the influences of dust aerosols on climate and human daily activities, dust detection plays a crucial role in environmental and climatic studies. Detection of dust storms is critical to accurately understand dust, their properties and distribution. Currently, remotely sensed data such as MODIS (Moderate Resolution Imaging Spectroradiometer) with appropriate temporal and spectral resolutions have been widely used for this purpose. This paper investigates the capability of two physical-based methods, and random forests (RF) classifier, for the first time, to detect dust storms using MODIS imagery. Since the physical-based approaches are empirical, they suffer from certain drawbacks such as high variability of thresholds depending on the underlying surface. Therefore, classification-based approaches could be deployed as an alternative. In this paper, the most relevant bands are chosen based on the physical effects of the major classes, particularly dust, cloud and snow, on both emissive infrared and reflective bands. In order to verify the capability of the methods, OMAERUV AAOD (aerosol absorption optical depth) product from OMI (Ozone Monitoring Instrument) sensor is exploited. In addition, some small regions are selected manually to be considered as ground truth for measuring the probability of false detection (POFD) and probability of missing detection (POMD). The dust class generated by RF is consistent qualitatively with the location and extent of dust observed in OMERAUV and MODIS true colour images. Quantitatively, the dust classes generated for eight dust outbreaks in the Middle East are found to be accurate from 7% and 6% of POFD and POMD respectively. Moreover, results demonstrate the sound capability of RF in classifying dust plumes over both water and land simultaneously. The performance of the physical-based approaches is found weaker than RF due to empirical thresholds that are not always true.

Effects of Biomass Burning Emissions on Air Quality Over the Continental USA: A Three-Year Comprehensive Evaluation Accounting for Sensitivities Due to Boundary Conditions and Plume Rise Height

We report a comprehensive evaluation of the impacts of biomass burning on regional ozone and fine particulate matter (PM2.5) over the continental USA, southern Canada, and northern Mexico during 2012–2014 using the Community Multiscale Air Quality (CMAQ) chemical transport model. Inputs included the Fire INventory from National Center for Atmospheric Research (FINN) for fire emissions, Biogenic Emission Inventory System (BEIS) for biogenics, the US Environmental Protection Agency (USEPA)’s National Emissions Inventory of 2011 (NEI2011) for anthropogenic sources, and Weather Research and Forecasting (WRF) model fields for meteorology. In situ data were taken from the Texas Commission on Environmental Quality (TCEQ)’s Continuous Ambient Monitoring Stations (CAMS) and the USEPA’s Air Quality System (AQS) networks. This study has marked improvements over the previous biomass burning evaluations, which are as follows: (a) a significantly longer simulation episode; (b) use of 3-D dynamic boundary conditions; (c) grid nudging to improve meteorological fields; and (d) physically representative fire plume rise model. Observations showed ozone hot spots of 60–70 parts per billion (ppb) across the Western Mountain region and California. The model was able to reproduce these only in 2012, underpredicting in California otherwise. Monthly mean biomass impacts of 2–3 ppb, averaged over daylight hours (6:00–18:00 CST), were predicted for California and Idaho in 2012 and 2013. The largest impacts were predicted for summer 2013, adding 3 ppb in northern Mexico and southeastern Canada, and 1 ppb in Florida, New Mexico, and Colorado. For April 2014, the model predicted 1–2 ppb disparities in ozone over the southern USA; a 1–2 ppb impact in southeastern Oregon, northwestern Nevada, and southern Idaho during July 2014; and in August, up to 3 ppb changes in western California, Central Oregon, Idaho, southwestern Canada, and southern Georgia. The model was unable to accurately capture the high PM2.5 concentrations across the domain. Large monthly mean fire impacts of up to 10 μg m−3 were predicted for southeastern Canada in July 2012 and June and July 2013, and for Alabama, Georgia, Idaho, and southwestern Canada for October 2013. In June 2014, the model significantly underpredicted when the biomass impact was minimal, indicating that uncertainty in biomass emissions was not the probable cause for model-measurement error.

First Top‐Down Estimates of Anthropogenic NOx Emissions Using High‐Resolution Airborne Remote Sensing Observations

A number of satellite‐based instruments have become an essential part of monitoring emissions. Despite sound theoretical inversion techniques, the insufficient samples and the footprint size of current observations have introduced an obstacle to narrow the inversion window for regional models. These key limitations can be partially resolved by a set of modest high‐quality measurements from airborne remote sensing. This study illustrates the feasibility of nitrogen dioxide (NO_2) columns from the Geostationary Coastal and Air Pollution Events Airborne Simulator (GCAS) to constrain anthropogenic NO_x emissions in the Houston‐Galveston‐Brazoria area. We convert slant column densities to vertical columns using a radiative transfer model with (i) NO_2 profiles from a high‐resolution regional model (1 × 1 km^2) constrained by P‐3B aircraft measurements, (ii) the consideration of aerosol optical thickness impacts on radiance at NO_2 absorption line, and (iii) high‐resolution surface albedo constrained by ground‐based spectrometers. We characterize errors in the GCAS NO_2 columns by comparing them to Pandora measurements and find a striking correlation (r > 0.74) with an uncertainty of 3.5 × 10^(15) molecules cm^(−2). On 9 of 10 total days, the constrained anthropogenic emissions by a Kalman filter yield an overall 2–50% reduction in polluted areas, partly counterbalancing the well‐documented positive bias of the model. The inversion, however, boosts emissions by 94% in the same areas on a day when an unprecedented local emissions event potentially occurred, significantly mitigating the bias of the model. The capability of GCAS at detecting such an event ensures the significance of forthcoming geostationary satellites for timely estimates of top‐down emissions.

Quantifying the Impact of Biomass Burning Emissions on Major Inorganic Aerosols and Their Precursors in the U.S.

The primary sources for inorganic aerosols from biomass burning are rather negligible; but they are predominantly formed chemically following emission of their precursors (e.g., SO2, NH3, HOx, and NOx). The biomass burning contributions to some of the precursors can be considerable. Accordingly, we quantify the impact of the emissions on major inorganic aerosols in April-October 2012-2014 using a regional model simulation verified by extensive surface observations throughout the US. Simulated CO enhancements on an hourly basis are used to classify the US into weak-moderate (5 20 ppbv). This separation not only facilitates the identification of the spatial frequency of the impact but also helps to filter out non-impacted periods, enabling us to focus on long-term contributions. Despite the nonlinear responses of several trace gases to emissions, we observe increases (weak-moderate, strong) in daily surface SO42- (1.16±0.32, 6.57±4.65 nmol/m3), NO3- (0.36±0.63, 4.70±7.05 nmol/m3) and NH4+ (2.70±0.92, 17.82±15.17 nmol/m3) on a national scale. These primarily resulted from i) increases in daily surface SO2 (0.02±0.01, 0.10±0.07 ppbv), afternoon OH (1.28±4.24, 12.82±23.76 ppqv), and H2O2 (0.06±0.02, 0.10±0.08 ppbv), which may have accelerated the conversion of S(IV) to S(VI), and ii) increases in daily surface NH3 (1.08±0.73, 8.61±7.73 nmol/m3) and HNO3 (1.44±0.48, 7.15±4.25 nmol/m3), which could have produced more particle-phase NH4NO3. In the West, where atmospheric moisture is limited, enhanced SO42- leaves less available water for NH4NO3 to become ions. Our results suggest that the major inorganic aerosols enhancement (mass) can reach to 23% of that of the carbonaceous aerosols.