Role of atmospheric nutrient pollution in stimulating phytoplankton growth in small area and shallow depth water bodies: Arabian Gulf and the sea of Oman Publication date: 15 December 2019 Source: Atmospheric Environment, Volume 219 Author(s): Ashraf Farahat, Abdelgadir Abuelgasim Abstract Limited investigations into the role of dust deposition in enhancing phytoplankton growth in small and shallow water areas have been reported in the remote sensing literature. In this work, we show that phytoplankton growth was stimulated by nutrients supplied by dust deposition over sea water following three major dust storms that blew over the Arabian Gulf (AG) and the Sea of Oman (SO). Shallow water conditions, as those found in the AG and SO, limit convection and the role of mixing processes in supplying nutrients and in mediating bloom growth. Using satellite data, we analyzed three major dust events over the AG and SO in 2009, 2012, and 2015, and the phytoplankton bloom enhancement that ensued. We used the Mixed Layer Depth model to simulate water mixing and convection currents during and after the high dust events. We also applied the Regional Climate Model RegCM 4.5 to derive dust depositions patterns over AG and SO following the dust outbreak. Additionally, we computed potential requirement versus supply of nitrogen, phosphorus, and iron nutrients to support the observed phytoplankton growth using published nutrient data. Carbon to nitrogen to phosphorus, and carbon to chlorophyll ratios were obtained from in situ measurements in the AG and SO. Shallow depth mixed layers can likely still supply phosphate, but not enough nitrate and iron, leading to potential nitrate and dissolved iron limitations. Our work shows that dust storms are playing a significant role in providing nitrate supplies to support phytoplankton growth in shallow waters such as the AG and SO.

The evaluation of mixing methods in HYSPLIT using measurements from controlled tracer experiments Publication date: 15 December 2019 Source: Atmospheric Environment, Volume 219 Author(s): Fong Ngan, Christopher P. Loughner, Ariel Stein Abstract The HYSPLIT dispersion model has different options to estimate the turbulent mixing depending on the availability of stability and turbulent parameters in the meteorological data. Dispersion simulations using different mixing options were conducted to simulate two controlled tracer experiments – the Project Sagebrush phase 1 (PSB1) for the sub-kilometer transport and the Cross Appalachian Tracer Experiment (CAPTEX) for the long-range transport. Through the comparisons of velocity variance and the evaluations of tracer concentrations, we evaluated different estimations of the turbulent velocity variance affecting the dispersion results. The mixing options in HYSPLIT are the Belijaars-Holtslag (BH) method, the Kantha-Clayson (KC) method, the turbulent kinetic energy (TKED) option, and the turbulent exchange coefficient (EXCH) option. The KC and EXCH method produced a larger maximum of the vertical velocity variance and at a higher altitude than other mixing options did. The vertical velocity variance profile of the BH scheme had a sharp increase from the surface to the height of the maximum values. The TKED option generated a flat profile with the smallest variation in its value with height. The plumes generated by the BH and TKED method (weaker mixing) had higher concentrations near the surface than those driven by the KC and EXCH option (stronger mixing). The statistical rank for the dispersion result using the TKED option was slightly better than others while the BH mixing generated results with a roughly worse rank. No mixing option always outperformed the other options. HYSPLIT users can select a mixing option according to the scenario and availability of meteorological fields, and use different options to generate dispersion ensembles.

Evaluating the use of satellite observations to supplement ground-level air quality data in selected cities in low- and middle-income countries Publication date: 1 December 2019 Source: Atmospheric Environment, Volume 218 Author(s): Matthew J. Alvarado, Amy E. McVey, Jennifer D. Hegarty, Eben S. Cross, Christa A. Hasenkopf, Richard Lynch, Edward J. Kennelly, Timothy B. Onasch, Yewande Awe, Ernesto Sanchez-Triana, Gary Kleiman Abstract Using data from ground-level measurements, this work evaluates the performance of one chemical transport model (CTM)-based approach using MERRA2 model output and one statistical approach using a generalized additive model to translate remotely-sensed Aerosol Optical Depth (AOD) measurements from the MODIS combined Deep Blue and Dark Target algorithm to surface-level PM2.5 concentrations for nine cities in low and middle-income countries that include a range of environmental conditions (e.g., mountainous, dusty, or coastal conditions) and only have between 1 and 10 ground level monitoring sites available. This evaluation shows that the CTM-based and statistical approaches tested here generally had a low correlation with the true daily-average PM2.5 values within these nine cities, in contrast to previous studies that had showed stronger correlations over other regions. In addition, the uncertainty in the satellite-based estimates of the daily-average PM2.5 concentration at a given location in a city tended to be very large (21–77% for the statistical methods, and 48–85% for the CTM-based methods). Many cities also had significant limitations in the availability of satellite observations of aerosols throughout the year due to their coastal location, persistent clouds, or persistent seasonal snow cover. The satellite-based methods tested in this work appear to work best for low altitude, inland cities like Hanoi and Delhi, but still have significant errors (43–60%) in predictions of daily-average PM2.5 concentrations at sites within these cities. The CTM-based satellite approach tested here tended to underestimate PM2.5 in high-altitude cities (except for Addis Ababa, Ethiopia, where they overestimate). However, this work suggests that under some conditions, adding satellite data to ground-level monitoring (GLM) network data via co-kriging may reduce the number of GLM sites needed to characterize PM2.5 concentrations within a city, but this needs to be determined on a case-by-case basis. This evaluation is then used to make recommendations to countries with sparse GLM networks on how to incorporate the use of satellite observations in their PM2.5 monitoring and under what conditions satellite approaches are likely to be unsuccessful.

Estimating the spatial variability of fine particles at the neighborhood scale using a distributed network of particle sensors Publication date: 1 December 2019 Source: Atmospheric Environment, Volume 218 Author(s): Rakefet Shafran-Nathan, Yael Etzion, Ohad Zivan, David M. Broday Abstract Small-scale heterogeneity of airborne pollutants may have implications for accurate exposure estimation in environmental health studies. However, it has been studied thus far mainly near main roads and over relatively short periods. The emergence of low-cost miniature particle sensors enables deployment of multi-sensor nodes for studying the spatial variability of ambient pollutants at fine spatial scales for extended time periods. We carried out measurements of fine ambient particles, both in terms of particle number concentrations (PNC; 0.3 < d < 3 μm) and particle mass concentrations (PM2.5; 0.3 < d < 2.5 μm), for more than three months (Dec 2015–Mar 2016; N = 1953 h) using a distributed network of optical particle counters. The network consisted of seven nodes that were deployed in a residential urban area, five nodes in one neighborhood (~1.5 km2) and two nodes in neighboring neighborhoods. While collocated with a reference monitoring instrument the sensors' readings were highly correlated (Pearson's r > 0.9; RMSE ~5 μg m−3) and the variance of the observations when the reference PM2.5 measurements were <20 μg m−3 (~90% of the records) was very low. Significantly higher heterogeneity was observed during the sensor deployment in the neighborhood, suggesting spatial variability of airborne particles at the neighborhood scale. Studying the spatial variability during different conditions (meteorological, day of the week, time of day) revealed signatures of human activity, suggesting specific sources that possibly contribute to the observed inner-neighborhood variability. Graphical abstract

Extreme smoke event over the high Arctic Publication date: 1 December 2019 Source: Atmospheric Environment, Volume 218 Author(s): Keyvan Ranjbar, Norm T. O'Neill, Erik Lutsch, Emily M. McCullough, Yasmin AboEl-Fetouh, Peng Xian, Kim Strong, Vitali E. Fioletov, Glen Lesins, Ihab Abboud Abstract The intense western Canadian fires of August 2017 resulted in a (10-year) extreme, high-Arctic smoke event. The primary measurements employed to monitor smoke events were fine mode (FM) aerosol optical depths (AODs) derived from the measured AOD spectra of two AEROCAN/AERONET (CIMEL) sunphotometers at Eureka, Nunavut, Canada. The FM AOD attribution is argued to be a necessary condition for the presence of smoke. Various supporting information, including the correlation with smoke proxy (CO) retrievals, the high frequency (rapid diurnal variation) and the high amplitude nature of the FM AODs, ground-based backscatter lidar profiles, the redundancy of the double CIMEL retrievals, satellite remote sensing, aerosol modeling and backtrajectories indicated that the peak event was likely due to smoke from extreme pyroCb fires in British Columbia. The hypothesis that the FM AOD peak event was an extreme event was tested for a derived ensemble of fine mode events and their peaks over the 10-year sampling period. The results confirmed the hypothesis at the 0.001 level of significance. Important indicators that the 10-year ensemble of FM AOD events did indeed represent smoke were their high frequency and high amplitude FM nature, their occurrence during the Boreal forest fire and agricultural fire seasons in Canada and Asia, and their strong correlation with CO abundances retrieved from FTIR measurements (when sufficient FM AOD and CO statistics were available). In the process of accumulating climatological-scale, monthly-binned fine mode AOD statistics, we found moderate correlations with forest fire or agricultural fire emissions from the Boreal North American, Boreal Asia or Central Asia regions as well as with CO retrievals at Eureka. We argued that confounding factors constraining the monthly binned fine mode AOD vs emissions correlations were associated with the monthly-binned meteorological dynamics (with notable, event-level, exceptions) while confounding factors constraining fine mode AOD vs CO correlations included the different physio-optical nature of those smoke proxies (solar attenuation by fine mode particle scattering versus solar attenuation by molecular absorption). We also employed historic (2005-2010) AHSRL (Arctic High Spectral Resolution Lidar) profiles to estimate an optically averaged smoke plume height of ~3 to 3 ½ km during the spring and summer seasons.

Primary particulate matter emissions and estimates of secondary organic aerosol formation potential from the exhaust of a China V diesel engine Publication date: 1 December 2019 Source: Atmospheric Environment, Volume 218 Author(s): Hua Zhou, Hongwei Zhao, Jie Hu, Mengliang Li, Qian Feng, Jingyu Qi, Zongbo Shi, Hongjun Mao, Taosheng Jin Abstract Vehicle emissions contribute to ambient particulate matter (PM) pollution directly via emissions of PM and indirectly by secondary aerosol formation, as a result of trace gas emissions. In this paper, we determined the emission factors of primary pollutants and estimated the secondary organic aerosol (SOA) formation potential of a China V heavy-duty diesel engine tested under ETC (European Transient Cycle) and ESC (European Stationary Cycle) with different types of fuels using an engine dynamometer. Volatile organic compounds (VOCs) emission factors were 55.7–121 mg/kwh, while primary PM emission factors were 15.0–26.8 mg/kwh. These values were substantially lower than those of older diesel vehicles that met pre-China V standards. Based on the SOA yields of the measured VOCs, the SOA formation potential of diesel engines were estimated to be 2.8–15.9 mg/kg fuel. The ratios of potential SOA/primary PM ranged from 0.07 to 0.16. We further showed that VOCs emission factors, SOA formation potential and the ratios of SOA/primary PM were highly dependent on the test cycle, whereas the primary PM emission factors on both test cycles and fuel quality.

Potential of static sampling using solid-phase microextraction for the assessment of formaldehyde sorption on building materials Publication date: 1 December 2019 Source: Atmospheric Environment, Volume 218 Author(s): Herve Plaisance, Pierre Mocho, Alexandre Gross, Valerie Desauziers Abstract Formaldehyde is considered as a priority pollutant of indoor air due to its numerous indoor sources and health impact. Due to its physico-chemical properties, the interaction of gaseous formaldehyde with material surfaces is suspected to play an important role in the distribution and fate of this compound indoors. This paper proposes an experimental method providing several parameters characterizing the material/air exchanges for formaldehyde namely, the adsorption and desorption rate constants (kam and kdm) and the material/air equilibrium partition coefficient (Ke) and the initial gas-phase concentration in equilibrium with the material surface (Cieq0). These parameters are assessed in a closed system (glass cell) containing the material and by a static sampling using solid-phase microextraction (SPME) fibers for measuring gaseous concentrations at the material surface during the emission and adsorption phases. Compared to the available methods of determining these parameters described in the literature, this method has the following advantages: (1) Taking into account of sorption on the inner walls of cell in the calculation of the material sorption parameters; (2) An analytical solution assessing the adsorption and desorption rate constants simultaneously from data of the adsorption phase; (3) An assessment of these sorption parameters under experimental conditions close to those encountered in indoor environments; (4) a satisfying reproducibility of the measured sorption parameters. The main performance of SPME sampling was assessed. The applicability of this method was proven to compare the sorption behavior of formaldehyde towards floor coverings.

The role of biomass burning agricultural emissions in the Indo-Gangetic Plains on the air quality in New Delhi, India Publication date: 1 December 2019 Source: Atmospheric Environment, Volume 218 Author(s): Casey D. Bray, William H. Battye, Viney P. Aneja Abstract Agricultural residue burning in the Indo-Gangetic Plains (IGP) releases large amounts of reactive nitrogen, among other pollutants, into the atmosphere each year. This study focuses on rice paddy residue burning and wheat residue burning during October–November and April–May, respectively, in 2016 and 2017. Emissions of reactive nitrogen species (ammonia (NH3), nitrous oxide (N2O) and oxides of nitrogen (NOx = NO + NO2)) were estimated for the study period using a suite of satellite products from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the National Aeronautics and Space Administration (NASA) Aqua and Terra satellites. Emissions were compared against ambient concentrations of fine particulate matter (PM2.5) in New Delhi, India, to help determine the impact that these agricultural burns have on PM2.5, which is known to have numerous health and environmental impacts associated with prolonged exposure to elevated concentrations. Daily average measured concentrations of PM2.5 in New Delhi range from 22.43 μg m−3 to 718.94 μg m−3 (average 127.15 μg m−3 ± 95.23 μg m−3), with the daily average PM2.5 concentration exceeding the national ambient air quality standard of 60 μg m−3 approximately 75% of the time. Concentrations of PM2.5 were found to peak during October–November, which corresponds with rice paddy residue burning in the IGP. In addition to this, statistical regression models were created to predict average daily PM2.5 concentrations in New Delhi, India, based on emissions of NH3 and organic carbon (OC) in the IGP as well as meteorological conditions. The regression model predicted ambient PM2.5 concentrations ranging from 35 to 719 μg m−3. The average modeled concentrations of PM2.5 in New Delhi, India, were 111 μg m−3 (standard deviation: ± 23 μg m−3) during April/May and 207 ± 87 μg m−3 during October/November. Both regression models (for wheat residue burning and for rice paddy residue burning) were comparable to the average observations (normalized mean bias less than 0.1%).

Age-specific seasonal associations between acute exposure to PM2.5 sources and cardiorespiratory hospital admissions in California Publication date: 1 December 2019 Source: Atmospheric Environment, Volume 218 Author(s): Keita Ebisu, Brian Malig, Sina Hasheminassab, Constantinos Sioutas Abstract Numerous studies have explored the relationships between short-term exposure to fine particulate matter (PM2.5) and morbidity. However, few studies have investigated which PM2.5 sources and constituents contribute to the health associations, and even fewer studies are available which explored age or seasonal effect modification for the associations between PM2.5 sources and health. We explored age-specific associations between short-term exposure to PM2.5 chemical constituents and its sources, and hospital admissions in California. We linked hospital admission data (n = 1,679,094) with PM2.5 chemical constituents and source apportionment data for eight sites in California for the period of 2002–2009. Site-specific source apportionment was conducted using Positive Matrix Factorization, and five PM2.5 sources were commonly identified in most sites (biomass burning, soil, secondary ammonium nitrate, secondary ammonium sulfate, and vehicular emissions). Age-stratified Poisson time-series regression was conducted for each site, and the health risk estimates were combined to generate overall age-specific associations with cardiovascular- and respiratory-related hospital admissions. We further conducted seasonal interaction models to assess seasonal effect modification. An interquartile range increase in PM2.5 vehicular emissions was associated with increased risk of cardiovascular-related hospital admission at lag 0 (1.32% [95% confidence interval (CI): 0.16, 2.49]) for elderly people (≥65 years old). Exposure to PM2.5 vehicular emissions increased the risk of respiratory-related hospitalizations at lag 2 (3.58% [95% CI: 0.90, 6.33]) for children (0–18 years old). Risk estimates of PM2.5 total mass, vehicular emissions, and its related constituents (e.g., iron) for respiratory admissions were higher in the warm season among children. Heterogeneous seasonal estimates were not observed for other age groups. Our results suggest that short-term exposures to several PM2.5 sources and their related constituents are more harmful than exposures to other pollutants, particularly for children in summer. Identifying toxic sources is important for developing effective interventions and protecting susceptible populations. Graphical abstract

Simulation and analysis of causes of a haze episode by combining CMAQ-IPR and brute force source sensitivity method Publication date: 1 December 2019 Source: Atmospheric Environment, Volume 218 Author(s): Tu-Fu Chen, Ken-Hui Chang, Chiu-Hsuan Lee Abstract In other studies the Integrated Process Rate (IPR) method was widely used to analyze the impact of various physical and chemical processes and their relative significance on PM2.5 in pollution episodes for specific sites; however, sources of pollution and their effect in planning emission control strategies were not considered. Using results of a brute force source sensitivity (BFSS) method may complement IPR. This paper integrates CMAQ-IPR and BFSS to examine the causes of a PM2.5 episode on Jan 15, 2010 across two air quality monitoring stations in Taichung, Taiwan (Shalu, a rural station and Situn, an urban station). Two pollution deterioration events (time intervals) were observed and simulated. When only CMAQ-IPR was used for simulation, the results revealed most pollution (>60%) was from local sources at the Situn Station. The two deterioration intervals differed however, as interval 1 showed a contribution from local emissions (49%), vertical advection (41%) and aerosols processes (10%). Interval 2 at Shalu Station was categorized as a downwind monitoring station, where emission from the grid constituted only 37%, and the majority of pollutants travelling by horizontal advection (56%), and to some extent, aerosols process (6%). Simulation by integrating IPR and BFSS (including diesel vehicles, gasoline vehicles/motorcycles, construction/road dust, iron/steel industries, and power plants around Taiwan) rendered to the type of physical or chemical process in which pollutants went through and its impact on the concentration of PM2.5 in the air quality monitoring station, which helped planning of emission control measures. Simulation results indicated improvement of PM2.5 concentrations in Situn and Shalu would be limited (only 0.2–4.2% contribution to deteriorating PM2.5 levels at the stations) even the emissions of the power plants and iron/steel industries in whole Taiwan were under control. However, by controlling the emissions of diesel vehicles, gasoline vehicles/motorcycles, and construction/road dust in whole Taiwan, the improvent is much enhanced (49–79% contribution to deteriorating PM2.5 levels at the stations), but it cost a lot. Nonethless, the improvement was quite significant by controlling the local emissions (around the monitoring station) of diesel vehicles, gasoline vehicles/motorcycles, and construction/road dust for Situn Station as they accounted for 72–85% of these three sources contribution to deteriorating PM2.5 levels at the station. For Shalu Station, it required a wide area of controlling the emissions from diesel vehicles, gasoline vehicles/motorcycles, and construction/road dust because there was 22–53% of these three sources contribution to deteriorating PM2.5 levels at the station from horizontal or vertical advection. Graphical abstract