Urban Monitoring Sites Research Articles

Black Carbon along a Highway and in a Residential Neighborhood during Rush-Hour Traffic in a Cold Climate

Short-term exposure to ultra-fine Black Carbon (BC) particles produced during incomplete fuel combustion of wood and fossil fuel has been linked to respiratory and cardiovascular diseases, hospitalizations and premature deaths. The goal of this research was to assess traffic-related BC in a cold climate along an urban highway and 300 m into an adjacent residential neighborhood. BC was measured with an aethalometer (MA350, Aethlabs) along the main traffic artery in geothermally heated Reykjavík, the capital of Iceland (64.135° N–21.895° W, 230,000 inhabitants). Stationary monitoring confirmed that traffic was the dominant source of roadside BC in winter, averaging 1.0 ± 1.1 µg/m3 (0.6 and 1.1 µg/m3 median and interquartile range; 28,000 vehicles/day). Inter-day variations in BC were primarily correlated to the atmospheric lapse rate and wind speed, both during stationary and mobile campaigns. During winter stills, BC levels surpassed 10 µg/m3 at intersections and built up to 5 µg/m3 during the afternoon in the residential neighborhood (adjacent to the highway with 43,000 vehicles/day). The BC penetrated deeply into the neighborhood, where the lowest concentration was 1.8 µg/m3 within 300 m. BC concentration was highly correlated to nitrogen dioxide (r > 0.8) monitored at the local Urban Traffic Monitoring site.

Impact Of Meteorological Parameters On The Daily Variability Of The Ground-Level PM<sub>2.5</sub> Concentrations According To Measurements In The Middle Urals

The results of a comparison of the PM2.5 aerosol concentration daily variability for the summer and winter seasons at the urban and background monitoring sites in the Middle Urals for 2016–2019 are presented. The cluster analysis method revealed a statistically significant difference between the two groups corresponding to higher and lower concentrations of fine aerosol during the day. Studies of the daily variation of the PM2.5 particle concentration in the Middle Urals indicate the leading role of meteorological characteristics (in particular, air temperature, pressure and wind speed) in changing the level of aerosol suspension in the air surface layer. Distinctive typical average daily concentrations of PM2.5 for the Middle Urals region, corresponding to the cluster of lower values, are observed in the summer and are on average ~ 5.2 µg/m3 for the urban area and ~ 3.4 μg/m3 for the background site. In winter, these parameters are 12.8 μg/m3 for urban conditions and 10.5 μg/m3 for background site. The higher content of PM2.5 particles, corresponding to the cluster of higher values, are identified in winter and are on average ~32.2 µg/m3 in urban conditions and ~ 30.3 µg/m3 in the background area. In summer, these parameters are 13.6 μg/m3 for urban site and 9.6 μg/m3 for background area. Simultaneous analysis of the fine aerosol concentrations and the meteorological parameters in the surface atmospheric layer allowed to define of weather conditions, at which the occurrence of higher PM2.5 values is possible.

Estimating background concentrations of PM2.5 for urban air quality modelling in a data poor environment

Atmospheric dispersion models are widely applied to simulate pollutant concentrations such as PM2.5 for use in long- and short-term health studies. A significant proportion of PM2.5 originates outside urban areas in which many people live. It is important to reflect this ‘background’ component in the modelling process in order to provide an accurate representation of the total pollution load experienced by human populations. To be credible, model outputs must be verified against available monitoring data, which, in the case of PM2.5, may be limited to a small number of monitoring sites across a large urban area. Here we evaluate four different approaches to representing background PM2.5 in an atmospheric dispersion model (ADMS-Urban) for Nottingham, UK. A directional approach, based on multiple urban background monitoring sites located outside the study area provides the most robust estimates. Our adopted approach allows us to model both short- and long-term air quality conditions, whilst accounting for local- and regional-scale variations in the pollution burden, and will ultimately enable us to assess short- and long-term effects of air pollution on health.

Copper in airborne fine particulate matter (PM2.5) from urban sites causes the upregulation of pro-inflammatory cytokine IL-8 in human lung epithelial A549 cells.

Fine atmospheric particles, such as PM2.5, are strongly related to the onset and exacerbation of inflammatory responses leading to the development of respiratory and cardiovascular diseases. PM2.5 is a complex mixture of tiny particles with different properties (i.e., size, morphology, and chemical components). Moreover, the mechanism by which PM2.5 induces inflammatory responses has not been fully elucidated. Therefore, it is necessary to determine the composition of PM2.5 to identify the main factors causing PM2.5-associated inflammation and diseases. In the present study, we investigated PM2.5 from two sites (Fukue, a remote monitoring site, and Kawasaki, an urban monitoring site) with greatly different environments and PM2.5 compositions. The results of ICP-MS and EDX-SEM indicated that PM2.5 from Kawasaki contained more metals and significantly induced the expression of the pro-inflammatory cytokine gene IL-8 compared to the PM2.5 from Fukue. We also verified the increased secretion of IL-8 protein from exposure to PM2.5 from Kawasaki. We further investigated their effects on inflammatory response and cytotoxicity using metal nanoparticles (Cu, Zn, and Ni) and ions and found that the Cu nanoparticles caused a dose-dependent increase in IL-8 expression together with significant cell death. We also found that Cu nanoparticles enhanced the secretion of IL-8 protein. These results suggest that Cu in PM2.5 is involved in lung inflammation.

Urban Surface Ozone Concentration in Mainland China during 2015-2020: Spatial Clustering and Temporal Dynamics.

Urban ozone (O3) pollution in the atmosphere has become increasingly prominent on a national scale in mainland China, although the atmospheric particulate matter pollution has been significantly reduced in recent years. The clustering and dynamic variation characteristics of the O3 concentrations in cities across the country, however, have not been accurately explored at relevant spatiotemporal scales. In this study, a standard deviational ellipse analysis and multiscale geographically weighted regression models were applied to explore the migration process and influencing factors of O3 pollution based on measured data from urban monitoring sites in mainland China. The results suggested that the urban O3 concentration in mainland China reached its peak in 2018, and the annual O3 concentration reached 157 ± 27 μg/m3 from 2015 to 2020. On the scale of the whole Chinese mainland, the distribution of O3 exhibited spatial dependence and aggregation. On the regional scale, the areas of high O3 concentrations were mainly concentrated in Beijing-Tianjin-Hebei, Shandong, Jiangsu, Henan, and other regions. In addition, the standard deviation ellipse of the urban O3 concentration covered the entire eastern part of mainland China. Overall, the geographic center of ozone pollution has a tendency to move to the south with the time variation. The interaction between sunshine hours and other factors (precipitation, NO2, DEM, SO2, PM2.5) significantly affected the variation of urban O3 concentration. In Southwest China, Northwest China, and Central China, the suppression effect of vegetation on local O3 was more obvious than that in other regions. Therefore, this study clarified for the first time the migration path of the gravity center of the urban O3 pollution and identified the key areas for the prevention and control of O3 pollution in mainland China.

Spatial aspects of urban air quality management: Estimating the impact of micro-scale urban form on pollution dispersion

Urban planning and design solutions affect urban ventilation conditions, thus mitigating the effects of atmospheric pollution. However, these findings are not being implemented in the planning practice to a sufficient extent, partly due to the lack of specific guidelines. Moreover, many urban air quality monitoring (AQM) sites have low represnentativeness and thus do not provide comprehensive data for effective urban air pollution control with respect to the urban spatial policy. An integrated assessment method based on modelling, simulation, and geospatial data processing tools was used to investigate the impact of micro-scale urban form on the local ventilation conditions and pollution dispersion. The proposed approach combined computational fluid dynamics (CFD) and geographic information system (GIS) tools, and particularly the newly developed Residence Time Index (RTI) - a single CFD-derived parameter quantifying the capabilities of the micro-scale built environment to retain PM10 pollution. Urban segments around monitoring sites in three Polish cities (Gdańsk, Warsaw, and Poznań), characterised by relatively low density and varied urban form typologies, were investigated. The results indicate that in these conditions the following features of the urban form have the strongest correlation with the RTI: plan area density (λP), gross floor area ratio (λGFA), and occlusivity (Oc), making them useful indicators for urban air quality management. On the other hand, PM10 data from the AQM sites are rather poorly linked with urban form indicators, which suggests that in complex urban scenarios a higher spatial resolution of air quality data is required for shaping the spatial policy. The implications from this analysis are useful for the urban planning practice. The developed approach may be also a valuable decision support tool for the assessment of the spatial representativeness of AQM sites.

PM10 variation, composition, and source analysis in Tuscany (Italy) following the COVID-19 lockdown restrictions

Abstract. To control the spread of COVID-19, exceptional restrictive measures were taken in March 2020 that imposed a radical change on the lifestyle of millions of citizens around the world, albeit for a short period. The national lockdown, which lasted from 10 March to 18 May 2020 in Italy, was a unique opportunity to observe the variation in air quality in urban environments under conditions comprising almost total traffic restriction and a strong reduction in work activities. In this paper, the data from 17 urban monitoring sites in Tuscany are presented, and the PM and NO2 concentrations in the 2 months before the start of the lockdown and the 2 months after lockdown are compared with the corresponding months of the previous 3 years. The results show that the total loads of PM2.5 and PM10 (particulate matter with an aerodynamic diameter smaller than 2.5 and 10 µm, respectively) decreased, but they did not exhibit significant changes compared to previous years, whereas NO2 underwent a drastic reduction. For three of these sites, the chemical composition of the collected samples was measured using thermal–optical techniques, ion chromatography, and particle-induced X-ray emission analysis, and the application of multivariate positive matrix factorization analysis also allowed for PM10 source identification and apportionment. Using these analyses, it was possible to explain the low sensitivity of PM10 to the lockdown effects as being due to different, sometimes inverse, behaviors of the different sources that contribute to PM. The results clearly indicate a decline in pollution levels related to urban traffic and an increase in the concentration of sulfate for all sites during the lockdown period.

Urban Particulate Matter Hazard Mapping and Monitoring Site Selection in Nablus, Palestine

Few air pollution studies have been applied in the State of Palestine and all showed an increase in particulate matter concentrations above WHO guidelines. However, there is no clear methodology for selecting monitoring locations. In this study, a methodology based on GIS and locally calibrated low-cost sensors was tested. A GIS-based weighted overlay summation process for the potential sources of air pollution (factories, quarries, and traffic), taking into account the influence of altitude and climate, was used to obtain an air pollution hazard map for Nablus, Palestine. To test the methodology, eight locally calibrated PM sensors (AirUs) were deployed to measure PM2.5 concentrations for 55 days from 7 January to 2 March 2022. The results of the hazard map showed that 82% of Nablus is exposed to a high and medium risk of PM pollution. Sensors’ readings showed a good match between the hazard intensity and PM concentrations. It also shows an elevated PM2.5 concentrations above WHO guidelines in all areas. In summary, the overall average for PM2.5 in the Nablus was 48 µg/m3. This may indicate the effectiveness of mapping methodology and the use of low-cost, locally calibrated sensors in characterizing air quality status to identify the potential remediation options.

Field Calibration and Evaluation of an Internet-of-Things-Based Particulate Matter Sensor

This paper presents a field evaluation of IoT-enabled Plantower PMS5003 particulate matter sensors in Birmingham, United Kingdom. Commercial, off the shelf, sensors were adapted to utilise Low Power Wide Area Network (LPWAN) IoT technology enabling batteries to be used as a power source. The devices are capable of measuring and communicating data to an online platform with a battery life of ∼2 months, at a measurement interval of 15 min, allowing for automated air quality monitoring for extended periods at high density. The sensors demonstrate success at being integrated into a wireless sensor network, with a high presence of readings. The average correlation coefficients (r2) between raw PMS device data and reference instrumentation are 0.718, 0.703, and 0.543 for PM1, PM2.5, and PM10, respectively. The devices also demonstrate good intersensor consistency, with Pearson’s r values between pairs ranging from 0.92 to 0.99 across all size ranges. Relative humidity (RH) clearly influences the response of the sensors, especially for RH >85%, in keeping with previous laboratory evaluations and evaluations of similar devices. The development of a multi-linear correction factor that accounts for humidity effects on the performance of the sensors is described; using this model, Pearson’s r values range from 0.81 to 0.91 compared to 0.73–0.85 from uncorrected values. There is also some evidence of drift at high humidity over an 8-week period, suggesting that such sensors will (at least currently) need recalibration approximately bimonthly. The limit of detection (LoD) (1.60–4.75 μg m−3) calculated from this study also demonstrates that the sensors are suitable for capturing concentrations typical of a moderately polluted United Kingdom urban environment—LoDs of PM2.5 in this study would have allowed for capture of 94.7% of the concentrations recorded at a typical United Kingdom urban roadside monitoring site between 2017 and 2020.

Assessment of traffic-related air pollution (TRAP) at two near-road schools and residence in El Paso, Texas, USA

Traffic-related air pollution (TRAP) and associated health risks at near-road communities have been studies extensively. As part of a larger air pollution health effects study in children with asthma, this research used on-site air quality monitors to characterize air pollutants at two near-road schools (CW and B) and a residential house (H) in El Paso, TX. The study sites were near a major inter-state roadway (US-54). Air quality data for PM2.5, PM10, NO2, and O3 was collected for ten weeks in Fall (2017). The spatial and temporal variability in the pollutant concentrations was compared with air quality data from Texas Commission on Environmental Quality (TCEQ) sites. Similar trends in measured pollutants were observed across all examined sites. Higher concentrations of PM10 were recorded at school B. Spearman correlations, coefficient of divergence, and diurnal graphs helped characterize the differences in the pollutant levels between these sites. The results suggest a spatial and temporal variability between the sites examined and available TCEQ sites. It is recommended that studies performed in this high-altitude arid region employ on-site measurements to avoid exposure misclassification and erroneous estimations from using urban central ambient monitoring sites (CAMS).

Comparison of Volatile Organic Compound Concentrations in Ambient Air among Different Source Areas around Khon Kaen, Thailand

Volatile organic compounds (VOCs) are a complex group of chemicals that pose a direct risk to human health. They also lead to the formation of other air pollution constituents, including fine particulate matter (PM2.5) and ground level ozone (O₃). The ambient air concentrations of 19 VOCs were measured using multi-day 24 h sampling at two urban sites and two rural sites in the area of Khon Kaen, Thailand. Results showed that most VOCs were at concentrations considered acceptable according to the 24 h average standards established by the Thai Pollution Control Department. The VOC acrolein, however, was detected at concentrations (0.69–1.15 μg/m3) in excess of the 24 h average standard (0.55 μg/m3). Two other VOCs, benzene and 1,3-butadiene, were also detected at elevated levels (1.73–2.75 and 0.18–0.40 μg/m3, respectively) that indicated the potential to exceed the 1-year average standard. VOC concentrations were highest in the urban market monitoring site, suggesting that vehicle exhaust and food preparation using cooking oil at high temperatures may have been potential sources of the elevated VOCs.

Impacts of the COVID-19 economic slowdown on ozone pollution in the U.S.

In this work, we use observations and experimental emissions in a version of NOAA's National Air Quality Forecasting Capability to show that the COVID-19 economic slowdown led to disproportionate impacts on near-surface ozone concentrations across the contiguous U.S. (CONUS). The data-fusion methodology used here includes both U.S. EPA Air Quality System ground and the NASA Aura satellite Ozone Monitoring Instrument (OMI) NO2 observations to infer the representative emissions changes due to the COVID-19 economic slowdown in the U.S. Results show that there were widespread decreases in anthropogenic (e.g., NOx) emissions in the U.S. during March–June 2020, which led to widespread decreases in ozone concentrations in the rural regions that are NOx-limited, but also some localized increases near urban centers that are VOC-limited. Later in June–September, there were smaller decreases, and potentially some relative increases in NOx emissions for many areas of the U.S. (e.g., south-southeast) that led to more extensive increases in ozone concentrations that are partly in agreement with observations. The widespread NOx emissions changes also alters the O3 photochemical formation regimes, most notably the NOx emissions decreases in March–April, which can enhance (mitigate) the NOx-limited (VOC-limited) regimes in different regions of CONUS. The average of all AirNow hourly O3 changes for 2020–2019 range from about +1 to −4 ppb during March–September, and are associated with predominantly urban monitoring sites that demonstrate considerable spatiotemporal variability for the 2020 ozone changes compared to the previous five years individually (2015–2019). The simulated maximum values of the average O3 changes for March–September range from about +8 to −4 ppb (or +40 to −10%). Results of this work have implications for the use of widespread controls of anthropogenic emissions, particularly those from mobile sources, used to curb ozone pollution under the current meteorological and climate conditions in the U.S.

Daily levels of coarse inhalable particles and sick leaves

BACKGROUND AND AIM: Road traffic is typically the main source of coarse inhalable particles (2.5 µmdiameter10 µm) in urban areas. Consequently, exposure is high e.g. during commuting to work, in northern conditions especially during the spring time. Our aim was to evaluate whether daily outdoor levels of coarse inhalable particles are associated with the daily number of new sick leaves in Helsinki, Finland. METHODS: Data on work absenteeism among the employees of the city of Helsinki (approx. 50 000) was obtained for the years 2001-2017. Concentration of coarse inhalable particles was calculated by subtracting daily fine particle concentration from the concentration of inhalable particles, both measured at an urban background monitoring site. Statistical analyses were conducted using generalized additive quasi-Poisson regression, separately for the whole year and the spring dust period (15.3.-15.5). RESULTS:There were almost 2 million sick leaves in the data. The same day coarse particle concentration was associated with an increased risk of sick leave: relative risk for a 10 µg/m increase in exposure was 1.018 (95% confidence intervals 1.005-1.030). Fine particles had similar effect but with one day lag. During the spring dust period, coarse particles were not associated with the outcome. CONCLUSIONS:Exposure to road dust seems to increase the risk of work absenteeism. However, during the spring dust period particle toxicity may be reduced e.g. because of aging of the material during winter. KEYWORDS: air pollution, coarse particles, work absenteeism, short-term

Sources of Formaldehyde in Bountiful, Utah

The U.S Environmental Protection Agency’s National Air Toxics Trends Stations Network has been measuring the concentration of hazardous air pollutants (HAPs) including formaldehyde (HCHO) since 2003. Bountiful, Utah (USA) has served as one of the urban monitoring sites since the network was established. Starting in 2013, the mean concentration of HCHO measured in Bountiful, Utah exceeded the non-cancer risk threshold and the 1 in 1 million cancer risk threshold. In addition, the measured concentrations were more than double those found at surrounding locations in Utah. A Positive Matrix Factorization (PMF) analysis using PMF-EPA v5 was performed using historical data (2004–2017) to better understand the sources of formaldehyde in the region. The historical data set included samples that were collected every sixth day on a 24 h basis. Beginning in February 2019 an eight-week air sampling campaign was initiated to measure formaldehyde on a two-hour averaged basis. In addition, the measurements of O3, NO, NO2, benzene, toluene, ethylbenzene, and xylenes (BTEX) were also collected. Corresponding back-trajectory wind calculations for selected time periods were calculated to aid in the understanding of the effects of BTEX emission sources and formaldehyde formation. The results indicate that the principal formaldehyde sources are associated with biomass burning and the conversion of biogenic emissions into HCHO. Back-trajectory wind analysis of low (≤3 ppbv) and high (23.8–32.5 ppbv) HCHO cases show a clear dominance of high HCHO originating in trajectories that come from the southwest and pass over the area of the oil refineries and industrial sources in the north Salt Lake City area.

Operational bias correction for PM2.5 using the AIRPACT air quality forecast system in the Pacific Northwest

ABSTRACT A bias correction scheme based on a Kalman filter (KF) method has been developed and implemented for the AIRPACT air quality forecast system which operates daily for the Pacific Northwest. The KF method was used to correct hourly rolling 24-h average PM2.5 concentrations forecast at each monitoring site within the AIRPACT domain and the corrected forecasts were evaluated using observed daily PM2.5 24-h average concentrations from 2017 to 2018. The evaluation showed that the KF method reduced mean daily bias from approximately −50% to ±6% on a monthly averaged basis, and the corrected results also exhibited much smaller mean absolute errors typically less than 20%. These improvements were also apparent for the top 10 worst PM2.5 days during the 2017–2018 test period, including months with intensive wildfire events. Significant differences in AIRPACT performance among urban, suburban, and rural monitoring sites were greatly reduced in the KF bias correction forecasts. The daily 24-h average bias corrections for each monitoring site were interpolated to model grid points using three different interpolation schemes: cubic spline, Gaussian Kriging, and linear Kriging. The interpolated results were more accurate than the original AIRPACT forecasts, and both Kriging methods were better than the cubic spline method. The Gaussian method yielded smaller mean biases and the linear method yielded smaller absolute errors. The KF bias correction method has been implemented operationally using both Kriging interpolation methods for routine output on the AIRPACT website (http://lar.wsu.edu/airpact). This method is relatively easy to implement, but very effective to improve air quality forecast performance. Implications: Current chemical transport models, including CMAQ, used for air quality forecasting can have large errors and uncertainties in simulated PM2.5 concentrations. In this paper, we describe a relatively simple bias correction scheme applied to the AIRPACT air quality forecast system for the Pacific Northwest. The bias correction yields much more accurate and reliable PM2.5 results compared to the normal forecast system. As such, the operational bias corrected forecasts will provide a much better basis for daily air quality management by agencies within the region. The bias corrected results also highlight issues to guide further improvements to the normal forecast system.

Quantification of Non-Exhaust Particulate Matter Traffic Emissions and the Impact of COVID-19 Lockdown at London Marylebone Road

This research quantifies current sources of non-exhaust particulate matter traffic emissions in London using simultaneous, highly time-resolved, atmospheric particulate matter mass and chemical composition measurements. The measurement campaign ran at Marylebone Road (roadside) and Honor Oak Park (background) urban monitoring sites over a 12-month period between 1 September 2019 and 31 August 2020. The measurement data were used to determine the traffic increment (roadside–background) and covered a range of meteorological conditions, seasons, and driving styles, as well as the influence of the COVID-19 “lockdown” on non-exhaust concentrations. Non-exhaust particulate matter (PM)10 concentrations were calculated using chemical tracer scaling factors for brake wear (barium), tyre wear (zinc), and resuspension (silicon) and as average vehicle fleet non-exhaust emission factors, using a CO2 “dilution approach”. The effect of lockdown, which saw a 32% reduction in traffic volume and a 15% increase in average speed on Marylebone Road, resulted in lower PM10 and PM2.5 traffic increments and brake wear concentrations but similar tyre and resuspension concentrations, confirming that factors that determine non-exhaust emissions are complex. Brake wear was found to be the highest average non-exhaust emission source. In addition, results indicate that non-exhaust emission factors were dependent upon speed and road surface wetness conditions. Further statistical analysis incorporating a wider variability in vehicle mix, speeds, and meteorological conditions, as well as advanced source apportionment of the PM measurement data, were undertaken to enhance our understanding of these important vehicle sources.

Modeled and measured near-road PM2.5 concentrations: Indianapolis and Providence cases

Modeling analyses were developed to evaluate near-road PM2.5 concentrations predicted by the AERMOD dispersion modeling chain under real-world conditions, and to assess the sensitivity of modeled near-road concentrations to the choice of dispersion model (AERMOD or CAL3QHCR), meteorological data, and travel data processing approach. We evaluate PM2.5 monitoring sites near major freeways in Indianapolis, Indiana (for 2016) and Providence, Rhode Island (for 2015–2016). The modeling analyses are built upon bottom-up estimates of temporally and spatially resolved roadway PM2.5 emissions based on traffic monitoring data and local vehicle fleet emission factors. The dispersion model simulations use local meteorological data collected at or close to the near-road monitoring sites. Predictions involved a modeling chain that included travel activity data processing, emissions modeling (MOVES and AP-42), and air quality dispersion modeling. We estimated the difference between PM2.5 concentrations at the near-road monitor and at nearby urban air quality monitoring sites (the measured near-road “increment”), and compared modeled results to the measured increments. Based on monitoring data, estimates of multi-day-averaged near-road PM2.5 increments were 0.9 ± 0.6 μg/m3 at Indianapolis and 1.4 ± 0.2 μg/m3 at Providence (where the uncertainty represents the 95% confidence interval on the mean value), and were comparable to measured PM2.5 increments at these sites in the near-road literature. Modeled roadway contributions to multi-day-averaged near-road concentrations substantially exceeded measured values based on the near-road monitoring data. The average near-road PM2.5 increment modeled with AERMOD was more than 300% (factor of four) larger than the measured increment at Indianapolis, and more than 500% (factor of six) larger than the measured increment at Providence. These biases reflect cumulative uncertainty throughout the near-road PM2.5 modeling chain. The emissions modeling component may have contributed to the modeling chain biases in two ways. First, the relative contribution of modeled non-exhaust emissions (PM2.5 brake wear, tire wear, and re-suspended road dust) compared to tailpipe exhaust emissions was higher than what has been documented in several published studies. Second, other research findings indicate that the U.S. EPA MOVES2014 model may over-predict tailpipe PM2.5 exhaust. The dispersion modeling component may have also contributed to the modeling chain biases. For example, when local meteorological data were used, AERMOD results were relatively insensitive to wind direction on a daily averaged basis; as a result, modeled concentrations exceeded measured values regardless of whether the near-road monitor was upwind or downwind of the roadway. In summary, this work provides a unique evaluation of PM2.5 concentrations predicted by the near-road modeling chain, and provides valuable information to understand potential sources of uncertainty in the near-road modeling process.

Time-Dependent Downscaling of PM2.5 Predictions from CAMS Air Quality Models to Urban Monitoring Sites in Budapest

Budapest, the capital of Hungary, has been facing serious air pollution episodes in the heating season similar to other metropolises. In the city a dense urban air quality monitoring network is available; however, air quality prediction is still challenging. For this purpose, 24-h PM2.5 forecasts obtained from seven individual models of the Copernicus Atmosphere Monitoring Service (CAMS) were downscaled by using hourly measurements at six urban monitoring sites in Budapest for the heating season of 2018–2019. A 10-day long training period was applied to fit spatially consistent model weights in a linear combination of CAMS models for each day, and the 10-day additive bias was also corrected. Results were compared to the CAMS ensemble median, the 10-day bias-corrected CAMS ensemble median, and the 24-h persistence. Downscaling reduced the root mean square error (RMSE) by 1.4 µg/m3 for the heating season and by 4.3 µg/m3 for episodes compared to the CAMS ensemble, mainly by eliminating the general underestimation of PM2.5 peaks. As a side-effect, an overestimation was introduced in rapidly clearing conditions. Although the bias-corrected ensemble and model fusion had similar overall performance, the latter was more efficient in episodes. Downscaling of the CAMS models was found to be capable and necessary to capture high wintertime PM2.5 concentrations for the short-range air quality prediction in Budapest.

Temporal and spatial variability of atmospheric ammonia in the Lombardy region (Northern Italy)

This work investigates the spatial and temporal variability of atmospheric ammonia concentrations in the Lombardy region in Northern Italy, where its continuous measurement at hourly resolution began in 2007 at monitoring sites representative of three different land use areas (urban, rural, and mountain locations). Ammonia concentration data have been jointly analyzed with wind direction and speed to highlight the association between the origin of the air masses and the concentration levels observed at the monitoring sites far from the primary sources, essentially consisting of farming activities and cattle and pigs breeding located in the southeastern part of the Lombardy region. The annual average concentrations of ammonia observed at urban (4.4–13.4 μg m−3 range) and rural (17–35 μg m−3 range) monitoring sites are in substantial agreement with literature data, which are however limited and strongly influenced by the measurement techniques used. The lowest concentration levels (1.1–5.8 μg m−3 range) are observed at the monitoring sites in the mountain areas. Both the seasonal and daily time patterns of the concentrations appear strongly related to some features of the measurement sites, namely with regard to their exposure to agricultural activities, whose seasonal practices arise in emissions responsible for strong variations in the ammonia atmospheric levels. Conversely, in the mountain areas in the north of region, local conditions affecting the atmospheric circulation seem to play a more important role than local sources, with the highest concentrations occurring when the breezes transport NH3-rich air masses from the southern part of the region.

The Mechanisms Responsible for the Interactions among Oxalate, pH, and Fe Dissolution in PM2.5

In this study, 10-year records of PM2.5 oxalate concentrations in six urban and rural Canadian monitoring sites are analyzed along with the time series of other water-soluble ionic components, water-soluble metals, and aerosol pH to identify the major factors influencing its abundance and speciation. Oxalate was found to have higher mass loadings relative to sulfate and nitrate when the temperature was higher and the aerosol was more acidic, which is counter to the gas/particle phase partitioning expected from the effective Henry’s Law constant. However, a strong correlation was found between oxalate and water-soluble Fe (WS-Fe), with a slope between 1.3 and 4.6, suggesting that the major forms of the particulate oxalate were in complexes with WS-Fe, whose concentration was also found to be significantly impacted by aerosol pH. When aerosol exhibits pH around 2, the proton-promoted dissolution of Fe leads to a strong relationship between WS-Fe and pH, indirectly supporting higher oxalate levels in more acidic particles. Meanwhile, higher aerosol pH in wintertime favors oxalate to fully partition into the particle phase, but higher WS-Fe concentration in aerosol liquid water was also often observed when aerosol had higher oxalate concentration. This phenomenon suggests that the chelating-promoted dissolution mechanism induced by oxalate can enhance Fe dissolution in wintertime. Our results reveal that the interactions among pH, oxalate. and Fe exhibit strong seasonality, and an improved understanding of these mechanisms is important for the environmental fate studies of both organic acids and metals.