Wintertime Air Pollution Research Articles

Exploring the impact of nocturnal boundary layer stability on wintertime air pollution in a highly polluted basin city using unsupervised learning classification

This study utilizes ten years of wintertime boundary layer meteorological and surface air quality observations to characterize the nocturnal boundary layer (NBL) stability and assess its relationship with air pollution in Taiyuan, a highly polluted basin city in China. An unsupervised learning feature extraction technique known as the self-organizing map (SOM) is applied to objectively classify nocturnal virtual potential temperature (VPT) profiles. The SOM-based classification scheme allows the representation of wintertime day-to-day NBL evolutions by just nine regimes. Special attention is given to four dominant regimes: weak to moderate stability regime (NBL1), cloudy moderate stability regime (NBL3), windy moderate stability regime (NBL7), and strong stability regime (NBL9). These dominant regimes have relatively higher occurrence frequencies (>10%), with the highest frequency associated with the strong stability regime (NBL9) at 25.2%. The diurnal cycles of selected pollutants (CO, NO2, SO2, and PM2.5) exhibit significant distinctions among the different NBL regimes. For instance, in the strong stability regime, CO, NO2, and PM2.5 show explosive growth in the evening due to the accumulation of primary pollutants. However, in the cloudy moderate stability regime, PM2.5 exhibits persistent slow growth throughout the day, likely due to secondary particle formation under high humidity and high SO2 conditions. These findings enhance our understanding of NBL meteorological impacts on surface air pollution in basin cities.

Individual Risk Factors of PM2.5 Associated With Wintertime Mortality in Urban Patients With COPD

Air pollution contributes to premature mortality, but potential impacts differ in populations with existing disease, particularly for individuals with multiple risk factors. Although COPD increases vulnerability to air pollution, individuals with COPD and other individual risk factors are at the intersection of multiple risks and may be especially susceptible to the effect of acute outdoor air pollution. What is the association between wintertime air pollution and mortality in patients with COPD and the modifying role of individual risk factors? This study evaluated 19,243 deceased veterans with prior COPD diagnosis who had resided in 25 US metropolitan regions (2016-2019). Electronic health records included patient demographic characteristics; smoking status; and comorbidities such as asthma, coronary artery disease (CAD), obesity, and diabetes. Using geocoded addresses, individuals were assigned wintertime fine particulate matter (particulate matter smaller than 2.5 μg in diameter [PM2.5]) and nitrogen dioxide air pollution exposures. Associations between acute air pollution and mortality were estimated by using a time-stratified case-crossover design with a conditional logistic model, and individual risk differences were assessed according to stratified analysis. A 1.05 (95%CI, 1.02-1.09) mortality risk was estimated for each 10 μg/m3 increase in daily wintertime PM2.5). Older patients and Black individuals displayed elevated risk. Obesity was a substantial air pollution-related mortality risk factor (OR, 1.11; 95%CI, 1.01-1.23), and the estimated risk for individuals with obesity plus CAD or obesity plus diabetes was 16%higher. Wintertime PM2.5 exposure was associated with elevated mortality risk in people with COPD, but individuals with multiple comorbidities, notably obesity, had high vulnerability. Our study suggests that obesity, CAD, and diabetes are understudied modifiers of air pollution-related risks for people with existing COPD.

Spatio-temporal variations and socio-economic drivers of air pollution: Evidence from 332 Chinese prefecture-level cities

Air pollution is the result of a complex interaction between natural and anthropogenic environmental conditions. Recently, what's the mutual correlation between air pollutants, and how the monthly socio-economic indicators influence air pollution at the prefecture-level cities are still lack of discussion. Based on monthly air pollutants concentrations and socio-economic indicators from January 2015 to September 2020, this study systematically explored the spatial-temporal characteristics of China's air pollutants and its socio-economic drivers. Results showed that annual mean concentrations of CO, NO2, SO2, PM10 and PM2.5 fell substantially from 2015 to 2020, decreasing by 37%, 27%, 63%, 39% and 40%, respectively, whereas O3 changed slightly. Summertime O3 and NO2 pollution in North China Plain, and wintertime air pollution in China remained the seriously polluted. PMs concentrations were significantly positively correlated with SO2, CO and NO2 concentrations. The increases in GDP per capita aggravated NO2 and PMs pollution in southeast coastal areas in spring and winter. The air pollution in North China Plain were severer in spring and winter induced by the monthly retail sales of social consumer goods. In the Fenwei Plain, the growing electricity consumption per GDP contributed to O3 and PM10 mitigation in summer and autumn. It is worth noting that the improvement of socio-economic level and energy consumption level showed a trend of exacerbating CO, NO2, and O3 pollution from 2015 to 2020. The comprehensive monthly analysis provides theoretical support for fine-scale monitoring and feasible control strategy formulating for achieving China's sustainable development.

Socioeconomic and Demographic Associations with Wintertime Air Pollution Exposures at Household, Community, and District Scales in Rural Beijing, China.

The Chinese government implemented a national household energy transition program that replaced residential coal heating stoves with electricity-powered heat pumps for space heating in northern China. As part of a baseline assessment of the program, this study investigated variability in personal air pollution exposures within villages and between villages and evaluated exposure patterns by sociodemographic factors. We randomly recruited 446 participants in 50 villages in four districts in rural Beijing and measured 24 h personal exposures to fine particulate matter (PM2.5) and black carbon (BC). The geometric mean personal exposure to PM2.5 and BC was 72 and 2.5 μg/m3, respectively. The variability in PM2.5 and BC exposures was greater within villages than between villages. Study participants who used traditional stoves as their dominant source of space heating were exposed to the highest levels of PM2.5 and BC. Wealthier households tended to burn more coal for space heating, whereas less wealthy households used more biomass. PM2.5 and BC exposures were almost uniformly distributed by socioeconomic status. Future work that combines these results with PM2.5 chemical composition analysis will shed light on whether air pollution source contributors (e.g., industrial, traffic, and household solid fuel burning) follow similar distributions.

Vertical Profiling of Fresh Biomass Burning Aerosol Optical Properties over the Greek Urban City of Ioannina, during the PANACEA Winter Campaign

Vertical profiling of aerosol particles was performed during the PANhellenic infrastructure for Atmospheric Composition and climatE chAnge (PANACEA) winter campaign (10 January 2020–7 February 2020) over the city of Ioannina, Greece (39.65° N, 20.85° E, 500 m a.s.l.). The middle-sized city of Ioannina suffers from wintertime air pollution episodes due to biomass burning (BB) domestic heating activities. The lidar technique was applied during the PANACEA winter campaign on Ioannina city, to fill the gap of knowledge of the spatio-temporal evolution of the vertical mixing of the particles occurring during these winter-time air pollution episodes. During this campaign the mobile single-wavelength (532 nm) depolarization Aerosol lIdAr System (AIAS) was used to measure the spatio-temporal evolution of the aerosols’ vertical profiles within the Planetary Boundary Layer (PBL) and the lower free troposphere (LFT; up to 4 km height a.s.l.). AIAS performed almost continuous lidar measurements from morning to late evening hours (typically from 07:00 to 19:00 UTC), under cloud-free conditions, to provide the vertical profiles of the aerosol backscatter coefficient (baer) and the particle linear depolarization ratio (PLDR), both at 532 nm. In this study we emphasized on the vertical profiling of very fresh (~hours) biomass burning (BB) particles originating from local domestic heating activities in the area. In total, 33 out of 34 aerosol layers in the lower free troposphere were characterized as fresh biomass burning ones of local origin, showing a mean particle linear depolarization value of 0.04 ± 0.02 with a range of 0.01 to 0.09 (532 nm) in a height region 1.21–2.23 km a.s.l. To corroborate our findings, we used in situ data, particulate matter (PM) concentrations (PM2.5) from a particulate sensor located close to our station, and the total black carbon (BC) concentrations along with the respective contribution of the fossil fuel (BCff) and biomass/wood burning (BCwb) from the Aethalometer. The PM2.5 mass concentrations ranged from 5.6 to 175.7 μg/m3, while the wood burning emissions from residential heating were increasing during the evening hours, with decreasing temperatures. The BCwb concentrations ranged from 0.5 to 17.5 μg/m3, with an extremely high mean contribution of BCwb equal to 85.4%, which in some cases during night-time reached up to 100% during the studied period.

Probing wintertime air pollution sources in the Indo-Gangetic Plain through 52 hydrocarbons measured rarely at Delhi & Mohali

During wintertime, the Indo-Gangetic Plain suffers from severe air pollution affecting several hundred million people. Here we present unprecedented measurements and source analyses of 52 NMHCs (25 alkanes, 16 aromatics, 10 alkenes and one alkyne) in the cities of Delhi and Mohali (300 km north of Delhi) during wintertime (Dec 2016–Jan 2017). NMHCs were measured using a thermal desorption gas chromatograph equipped with flame ionisation detectors with data traceable to WMO standards. The ten most abundant NMHCs that were measured were the same at both Delhi and Mohali: propane, n-butane, acetylene, ethane, toluene, i-butane, ethene, i-pentane, benzene and propene and accounted for >50% of total measured NMHC mass concentration (137 ± 5.8 μg m−3 in Mohali and 239 ± 7.7 μg m−3 in Delhi). Ambient NMHCs and calculated hydroxyl radical reactivity were approximately twice as high in Delhi relative to Mohali, and 2–12 times higher than most other mega-cities, except Lahore and Karachi. Using chemical source signatures, traffic and LPG usage emissions were identified as the major contributor of these reactive NMHCs at both sites during nighttime, with additional minor contributions of garbage burning in Mohali, and evaporative fuel and biomass burning emissions in Delhi. Comparison of NMHC/CO and NMHC/C2H2 ratios over Mohali and Delhi, to other cities, suggested gasoline/petrol-fuelled vehicles were major NMHC emitters within the traffic source. The data from both Mohali and Delhi suggest that a large fraction of the fleet comprised vehicles with older emission control in both Mohali and Delhi. Analyses revealed poor representation of propene, ethene and trimethylbenzenes in the emission inventory (EDGARv4.3.2) over Mohali and Delhi. This study provides key data and new insights into the sources of reactive NMHCs (lifetime < few days) that drive regional wintertime pollution through direct effects and the formation of secondary pollutants.

Characteristics and sources of volatile organic compounds during pollution episodes and clean periods in the Beijing-Tianjin-Hebei region

Volatile organic compounds (VOCs) play an important role in air pollution. In this study, we conducted comprehensive field observations to investigate wintertime air pollution in Beijing, Wangdu, and Dezhou in the Beijing-Tianjin-Hebei region during 2017 and 2018. The average VOC concentrations of the three sites were 35.6 ± 26.6, 70.9 ± 56.3, and 50.5 ± 40.0 ppbv, respectively. The species with the highest concentration were similar in all three sites and included ethane, ethylene, acetylene, acetone, and toluene. The VOC mixing ratios of the three sites showed synchronous growth during pollution episodes and were 1.2–2 times higher than those during clean periods. Moreover, the OH loss rates (LOH) during pollution episodes were 1.2–1.7 times that during clean periods. The crucial reactive species in the three sites were ethylene, propylene, and acetaldehyde, contributing approximately 70% to the total LOH during pollution periods. According to the source apportionment analysis, vehicle exhausts were the largest source of VOCs in Beijing, accounting for more than 50% of the total emissions. During the pollution episodes, Beijing's industrial emissions decreased, but the secondary and background sources increased. Coal combustion was significant (approximately 40%) in Wangdu and should therefore be prioritized in emission reduction policies. In Dezhou, industrial emissions had a considerable impact on the VOC mixing ratio during pollution periods and should therefore be prioritized. The backward trajectory analysis showed that VOCs from the southern region likely contribute to Beijing's VOC pollution, highlighting the importance of regional integration for air quality management.

Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning

Nitrogen dioxide (NO2) is an important air pollutant with negative health effects and a precursor of ozone and particulate matter responsible for photo-chemical smog and wintertime air pollution. To evaluate human exposure to NO2 for public health assessment, maps of near-surface NO2 concentrations at a high resolution of 100 m are desirable. In this study, we report hourly maps of gridded near-surface NO2 concentrations that are produced using an extreme gradient-boosted tree ensemble for an Alpine domain (Switzerland and northern Italy) spanning two years, from June 2018 to May 2020. To estimate the NO2 distribution at ground level, we used satellite observations of NO2 vertical column density, land use data, meteorological fields and topographical information to train models with in situ NO2 ground measurements. The best model with this approach captured up to 59% of hourly NO2 variation for 40 test stations in the domain with a mean absolute error of 7.69μg/m3, performing especially well for urban regions with dense sampling. We present the first hourly maps of NO2 concentrations that reveal previously unresolved spatio-temporal variations. Local interpretations of the machine learning model demonstrate that TROPOMI NO2 satellite observations make a strong contribution to the information content of the near-surface NO2 maps besides their relatively coarse resolution (3.5 × 5.5 km2) and the fact that they are only available once a day under cloud-free conditions. The COVID-19 pandemic lockdown presents a case study that offers new insights into the importance of satellite data that can partially re-mediate statistical models' unsusceptibility to unusual events (like changes due to political intervention) with regard to model training.

Multisensor and Multimodel Monitoring and Investigation of a Wintertime Air Pollution Event Ahead of a Cold Front Over Eastern China

AbstractGiven the limitations in pollutant measurements (e.g., coverage, observation errors) and air quality model uncertainties (e.g., with parameterizations and emissions), a multisensor and multimodel approach offers additional benefits compared to a single‐instrument and deterministic approach for monitoring, investigating, and predicting air pollution events. In this study, we use multisensors (including the spaceborne MODIS, OCO‐2, AIRS, and OMPS instruments as well as surface instruments) and multimodels (including WRF‐Chem and WRF‐CO2) to investigate a severe air pollution event on December 9, 2016 over eastern China. During this episode, a strong cold front moved southward. At the leading edge of the front, WRF‐CO2 simulates an enhanced XCO2 belt while WRF‐Chem simulates a belt of high PM2.5 concentration. The XCO2 and PM2.5 belts are generally colocated, due to coemission of CO2 and pollutants (or their precursors). Satellite observations including MODIS AOD, OCO‐2 XCO2, OMPS NO2, AIRS CO, and surface data confirm the simulated pollution and XCO2 belts. Later on, the front became distorted due to terrain blocking and mountain channel flows. Both observations and simulations show that the channel winds between Mountains Dabie and Huang transport the haze plume into Jiangxi province, enhancing pollution in the region. It is concluded that the multisensor (including space‐based and ground‐based instruments) and multimodel (e.g., WRF‐CO2, WRF‐Chem) approach can be used to collectively monitor and investigate air pollution events, given that emissions of the involved species have generally similar spatial distributions.

Characteristics and potential sources of wintertime air pollution in Linfen, China.

Linfen in China’s Shanxi Province suffers severe air pollution in winter. Understanding the characteristics of air pollution and providing scientific support to mitigate such pollution are urgent matters. This study investigated the variations of PM2.5, PM10, NO2, SO2, O3, and CO in Linfen between December 1, 2019 and February 29, 2020. The mean concentrations of PM2.5, PM10, NO2, SO2, MDA8 (the maximum daily 8-h average) O3, and CO were 106.2, 139.4, 47.2, 41.0, 57.0 μg m−3, and 1.8 mg m−3, respectively. Large amounts of pollutants emitted by coal burning, industry, vehicles, and residents contributed to air pollution. Unfavorable meteorological conditions, such as lower temperature, weaker wind, higher relative humidity, and reduced planetary boundary layer height, made the situation worse. Fireworks and firecrackers set off to celebrate traditional Chinese festivals caused the concentration of PM pollutants to spike, with the maximum daily mean concentration of PM2.5 reached 314 μg m−3 and the peak hourly value reached 378.0 μg m−3. Suspensions of commercial and social activities due to COVID-19 reduced anthropogenic emissions, mainly from industry and transportation, which decreased the level of air pollutants other than O3. Analyses involving backward trajectory cluster, the potential source contribution function, and concentration weighted trajectory demonstrated that PM2.5 pollution mainly came from local emissions in Shanxi Province and regional transport from Inner Mongolia, Shaanxi, Hebei, Henan, and Gansu provinces. Shanxi and its surrounding provinces should adopt measures such as tightening environmental management standards, promoting the use of renewable energy, and adjusting the transportation structure to reduce regional emissions. This study will help policy-makers draft plans and policies to reduce air pollution in Linfen.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10661-021-09036-8.

Indoor Air Quality, Information, and Socio-Economic Status: Evidence from Delhi

Delhi faces some of the world’s highest concentrations of PM2.5, the most damaging form of air pollution. Although awareness of outdoor air pollution is rising across the world, there is limited information on indoor air pollution (IAP) levels, particularly in heavily polluted cities like Delhi. Even less evidence exists on how IAP varies by socio-economic status (SES), and whether or not addressing information gaps can change defensive investments against IAP. In this paper, we deploy Indoor Air Quality Monitors (IAQMs) in thousands of Delhi households across varying socio-economic strata in order to document IAP levels during the peak wintertime air pollution period. Across high and low SES households, we document indoor PM2.5 levels that are: (1) extraordinarily high — more than 20 times World Health Organization (WHO) standards; (2) only 10 percent lower in high (versus low) SES households; and (3) significantly higher than levels reported by the nearest, outdoor government monitors, the main source of public information on air pollution in this setting. We then report on a field experiment that randomly assigned IAQMs, as well as an opportunity to rent an air purifier at a subsidized price, across medium and high SES homes during the 2019-20 winter season.

Deciphering wintertime air pollution upon the West Lake of Hangzhou, China

The West Lake of Hangzhou, a world famous landscape and cultural symbol of China, suffered from severe air quality degradation in January 2015. In this work, Random Forest (RF) and Recurrent Neural Networks (RNN) are used to analyze and predict air pollutants on the central island of the West Lake. We quantitatively demonstrate that the PM2.5 and PM10 were chiefly associated by the ups and downs of the gaseous air pollutants (SO2, NO2 and CO). Compared with the gaseous air pollutants, meteorological circumstances and regional transport played trivial roles in shaping PM. The predominant meteorological factor for SO2, NO2 and surface O3 was dew-point deficit. The proportion of sulfate in PM10 was higher than that in PM2.5. CO was strongly positively linked with PM. We discover that machine learning can accurately predict daily average wintertime SO2, NO2, PM2.5 and PM10, casting new light on the forecast and early warning of the high episodes of air pollutants in the future.

Study of Secondary Organic Aerosol Formation from Chlorine Radical-Initiated Oxidation of Volatile Organic Compounds in a Polluted Atmosphere Using a 3D Chemical Transport Model.

The impact of chlorine (Cl) chemistry on the formation of secondary organic aerosol (SOA) during a severe wintertime air pollution episode is investigated in this study. The Community Multiscale Air Quality (CMAQ) model v5.0.1 with a modified SAPRC-11 gas-phase mechanism and heterogeneous reactions for reactive chlorine species is updated to include the formation of chlorine radical (Cl•)-initiated SOA (Cl-SOA) from aromatic compounds, terpenes, and isoprene. Reported SOA yield data on Cl-SOA formation from environmental chamber studies are used to derive the mass yield and volatility data for the two-product equilibrium-partitioning model. The heterogeneous reaction of particulate chloride (pCl-) leads to a significant increase in the Cl• and hydroxyl radical (OH) concentrations throughout the domain. Monthly Cl-SOA concentrations range from 0.7 to 3.0 μg m-3, with increasing anthropogenic Cl emissions leading to higher Cl-SOA concentrations. Indirectly, this also leads to an increase of monthly SOA by up to 2.5-3.0 g μm-3 from the traditional OH oxidation pathways as well as the surface uptake of glyoxal and methylglyoxal. Increased OH concentrations, however, do not always lead to higher overall SOA concentrations in the entire domain. High OH reduces the lifetime of glyoxal/methylglyoxal (GLY/MGLY), making them less available to form SOA. In the Sichuan Basin (SCB) and part of Southwest China where high O3 concentrations meet high pCl emissions, a higher Cl•/OH ratio leads to net O3 loss from the Cl• + O3 reaction, thus reducing SOA formation from the O3 oxidation of volatile organic compounds (VOCs). Also, the competition between Cl• and OH for VOCs could lead to lower overall SOA because the molar yields of the semivolatile products in Cl-VOC reactions are lower than their OH + VOC reaction counterparts. When Cl• concentrations are further increased with higher emissions of Cl, precursor gases can be depleted and become the limiting factor in SOA formation. This study reveals the direct and indirect impacts of chlorine chemistry on SOA in polluted winter conditions, which are greatly affected by the Cl emissions, the ambient O3 level, and the availability of SOA precursors.

Effects of atmospheric circulations on the interannual variation in PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; concentrations over the Beijing–Tianjin–Hebei region in 2013–2018

Abstract. The Chinese government has made many efforts to mitigate fine particulate matter pollution in recent years by taking strict measures on air pollutant reduction, which has generated the nationwide improvements in air quality since 2013. However, under the stringent air pollution controls, how the wintertime PM2.5 concentration (i.e., the mass concentration of atmospheric particles with diameters less than 2.5 µm) varies and how much the meteorological conditions contribute to the interannual variations in PM2.5 concentrations are still unclear, and these very important for the local government to assess the emission reduction of the previous year and adjust mitigation strategies for the next year. The effects of atmospheric circulation on the interannual variation in wintertime PM2.5 concentrations over the Beijing–Tianjin–Hebei (BTH) region in the period of 2013–2018 are evaluated in this study. Generally, the transport of clean and dry air masses and an unstable boundary layer in combination with the effective near-surface horizontal divergence or pumping action at the top of the boundary layer benefits the horizontal or vertical diffusion of surface air pollutants. Instead, the co-occurrence of a stable boundary layer, frequent air stagnation, positive water vapor advection and deep near-surface horizontal convergence exacerbate the wintertime air pollution. Favorable circulation conditions lasting for 2–4 d are beneficial for the diffusion of air pollutants, and 3–7 d of unfavorable circulation events exacerbates the accumulation of air pollutants. The occurrence frequency of favorable circulation events is consistent with the interannual variation in seasonal mean PM2.5 concentrations. There is better diffusion ability in the winters of 2014 and 2017 than in other years. A 59.9 % observed decrease in PM2.5 concentrations in 2017 over the BTH region could be attributed to the improvement in atmospheric diffusion conditions. It is essential to exclude the contribution of meteorological conditions to the variation in interannual air pollutants when making a quantitative evaluation of emission reduction measurements.

Investigating wintertime air pollution in Hangzhou, China

Hangzhou, one of the most prosperous cities in China, suffered from severe air quality degradation in wintertime, and the ambient atmospheric particulate matter (PM) has become the most public-concerning air pollutant. In this work, a case study in wintry Hangzhou is made, for analysis of air pollutants and prediction of PM2.5/PM10 using two machine learning models, recurrent neural network (RNN) and random forest. The results signify that statistic-based and inventory-free machine learning is competently alternative to the inventory-predicted atmospheric models. Variable importance (VI) indicates that CO was the predominant factor for both PM2.5 and PM10. Dew-point deficit played an essential role in shaping gaseous air pollutants. Water vapor pressure and hydrostatic energy had trivial impact on atmospheric pollutants. RNN and random forest both show high accuracy in predicting PM2.5 and PM10. The inter-annual consistence of PM’s components is confirmed. A method to pinpoint whether the high episodes of PM were spawned by long-range transport or increase of gaseous pollutants (SO2, NO2, and CO) is proposed. Additionally, the possible chemical bond between CO and PM needs to be further investigated.

Anthropogenic control over wintertime oxidation of atmospheric pollutants.

During winter in the mid-latitudes, photochemical oxidation is significantly slower than in summer and the main radical oxidants driving formation of secondary pollutants, such as fine particulate matter and ozone, remain uncertain, owing to a lack of observations in this season. Using airborne observations, we quantify the contribution of various oxidants on a regional basis during winter, enabling improved chemical descriptions of wintertime air pollution transformations. We show that 25-60% of NOx is converted to N2O5 via multiphase reactions between gas-phase nitrogen oxide reservoirs and aerosol particles, with ~93% reacting in the marine boundary layer to form >2.5 ppbv ClNO2. This results in >70% of the oxidizing capacity of polluted air during winter being controlled, not by typical photochemical reactions, but from these multiphase reactions and emissions of volatile organic compounds, such as HCHO, highlighting the control local anthropogenic emissions have on the oxidizing capacity of the polluted wintertime atmosphere.

Wintertime spatial distribution of ammonia and its emission sources in the Great Salt Lake region

Abstract. Ammonium-containing aerosols are a major component of wintertime air pollution in many densely populated regions around the world. Especially in mountain basins, the formation of persistent cold-air pools (PCAPs) can enhance particulate matter with diameters less than 2.5 µm (PM2.5) to levels above air quality standards. Under these conditions, PM2.5 in the Great Salt Lake region of northern Utah has been shown to be primarily composed of ammonium nitrate; however, its formation processes and sources of its precursors are not fully understood. Hence, it is key to understanding the emission sources of its gas phase precursor, ammonia (NH3). To investigate the formation of ammonium nitrate, a suite of trace gases and aerosol composition were sampled from the NOAA Twin Otter aircraft during the Utah Winter Fine Particulate Study (UWFPS) in January and February 2017. NH3 was measured using a quantum cascade tunable infrared laser differential absorption spectrometer (QC-TILDAS), while aerosol composition, including particulate ammonium (pNH4), was measured with an aerosol mass spectrometer (AMS). The origin of the sampled air masses was investigated using the Stochastic Time-Inverted Lagrangian Transport (STILT) model and combined with an NH3 emission inventory to obtain model-predicted NHx (=NH3+pNH4) enhancements. Enhancements represent the increase in NH3 mixing ratios within the last 24 h due to emissions within the model footprint. Comparison of these NHx enhancements with measured NHx from the Twin Otter shows that modelled values are a factor of 1.6 to 4.4 lower for the three major valleys in the region. Among these, the underestimation is largest for Cache Valley, an area with intensive agricultural activities. We find that one explanation for the underestimation of wintertime emissions may be the seasonality factors applied to NH3 emissions from livestock. An investigation of inter-valley exchange revealed that transport of NH3 between major valleys was limited and PM2.5 in Salt Lake Valley (the most densely populated area in Utah) was not significantly impacted by NH3 from the agricultural areas in Cache Valley. We found that in Salt Lake Valley around two thirds of NHx originated within the valley, while about 30 % originated from mobile sources and 60 % from area source emissions in the region. For Cache Valley, a large fraction of NOx potentially leading to PM2.5 formation may not be locally emitted but mixed in from other counties.

Role of Ammonia on the Feedback Between AWC and Inorganic Aerosol Formation During Heavy Pollution in the North China Plain

AbstractAtmospheric NH3 plays a vital role not only in the environmental ecosystem but also in atmosphere chemistry. To further understand the effects of NH3 on the formation of haze pollution in Beijing, ambient NH3 and related species were measured and simulated at high resolutions during the wintertime Air Pollution and Human Health‐Beijing (APHH‐Beijing) campaign in 2016. We found that the total NHx (gaseous NH3+particle NH4+) was mostly in excess of the SO42−‐NO3−‐NH4+‐water equilibrium system during our campaign. This NHx excess made medium aerosol acidity, with the median pH value being 3.6 and 4.5 for polluted and nonpolluted conditions, respectively, and enhanced the formation of particle phase nitrate. Our analysis suggests that NH4NO3 is the most important factor driving the increasing of aerosol water content with NO3− controlling the prior pollution stage and NH4+ the most polluted stage. Increased formation of NH4NO3 under excess NHx, especially during the nighttime, may trigger the decreasing of aerosol deliquescence relative humidity even down to less than 50% and hence lead to hygroscopic growth even under RH conditions lower than 50% and the wet aerosol particles become better medium for rapid heterogeneous reactions. A further increase of RH promotes the positive feedback “aerosol water content‐heterogeneous reactions” and ultimately leads to the formation of severe haze. Modeling results by Nested Air Quality Prediction Monitor System (NAQPMS) show the control of 20% NH3 emission may affect 5–11% of particulate matter PM2.5 formation under current emissions conditions in the North China Plain.

On the contribution of nocturnal heterogeneous reactive nitrogen chemistry to particulate matter formation during wintertime pollution events in Northern Utah

Abstract. Mountain basins in Northern Utah, including the Salt Lake Valley (SLV), suffer from wintertime air pollution events associated with stagnant atmospheric conditions. During these events, fine particulate matter concentrations (PM2.5) can exceed national ambient air quality standards. Previous studies in the SLV have found that PM2.5 is primarily composed of ammonium nitrate (NH4NO3), formed from the condensation of gas-phase ammonia (NH3) and nitric acid (HNO3). Additional studies in several western basins, including the SLV, have suggested that production of HNO3 from nocturnal heterogeneous N2O5 uptake is the dominant source of NH4NO3 during winter. The rate of this process, however, remains poorly quantified, in part due to limited vertical measurements above the surface, where this chemistry is most active. The 2017 Utah Winter Fine Particulate Study (UWFPS) provided the first aircraft measurements of detailed chemical composition during wintertime pollution events in the SLV. Coupled with ground-based observations, analyses of day- and nighttime research flights confirm that PM2.5 during wintertime pollution events is principally composed of NH4NO3, limited by HNO3. Here, observations and box model analyses assess the contribution of N2O5 uptake to nitrate aerosol during pollution events using the NO3- production rate, N2O5 heterogeneous uptake coefficient (γ(N2O5)), and production yield of ClNO2 (φ(ClNO2)), which had medians of 1.6 µg m−3 h−1, 0.076, and 0.220, respectively. While fit values of γ(N2O5) may be biased high by a potential under-measurement in aerosol surface area, other fit quantities are unaffected. Lastly, additional model simulations suggest nocturnal N2O5 uptake produces between 2.4 and 3.9 µg m−3 of nitrate per day when considering the possible effects of dilution. This nocturnal production is sufficient to account for 52 %–85 % of the daily observed surface-level buildup of aerosol nitrate, though accurate quantification is dependent on modeled dilution, mixing processes, and photochemistry.

Particulate matter, nitrogen oxides, ozone, and select volatile organic compounds during a winter sampling period in Logan, Utah, USA

ABSTRACT Particulate matter mass (PM), trace gaseous pollutants, and select volatile organic compounds (VOCs) with meteorological variables were measured in Logan, Utah (Cache Valley), for >4 weeks during winter 2017 as part of the Utah Winter Fine Particle Study (UWFPS). Higher PM levels for short time periods and lower ozone (O3) levels were present due to meteorological and mountain valley conditions. Nitrogenous pollutants were relatively strongly correlated with PM variables. Diurnal cycles of NOx, O3, and fine PM(PM 2.5) (aerodynamic diameter <2.5 μm [PM2.5]) suggested formation from NOx. O3 levels increased from early morning into midafternoon, and NOx and PM2.5 increased throughout the morning, followed by sharp decreases. Toluene/benzene and xylenes/benzene ratios and VOC correlations with nitrogenous and PM species were indicative of local traffic sources. Wind sector comparisons suggested that pollutant levels were lower when winds were from nearby mountains to the east versus winds from northerly or southerly origins. Implications: The Cache Valley in Idaho and Utah has been designated a PM2.5 nonattainment area that has been attributed to air pollution buildup during winter stagnation events. To inform state implementation plans for PM2.5 in Cache Valley and other PM2.5 nonattainment areas in Utah, a state and multiagency federal research effort known as the UWFPS was conducted in winter 2017. As part of the UWFPS, the U.S. Environmental Protection Agency (EPA) measured ground-based PM species and their precursors, VOCs, and meteorology in Logan, Utah. Results reported here from the EPA study in Logan provide additional understanding of wintertime air pollution conditions and possible sources of PM and gaseous pollutants as well as being useful for future PM control strategies in this area.