Assessment of source contribution to air quality in an urban area close to a harbor: Case-study in Porto, Portugal

Predicting dust pollution from dry bulk ports in coastal cities: A hybrid approach based on data decomposition and deep learning

Dust pollution from storage and handling of materials in dry bulk ports seriously affects air quality and public health in coastal cities. Accurate prediction of dust pollution helps identify risks early and take preventive measures. However, there remain challenges in solving non-stationary time series and selecting relevant features. Besides, existing studies rarely consider impacts of port operations on dust pollution. Therefore, a hybrid approach based on data decomposition and deep learning is proposed to predict dust pollution from dry bulk ports. Port operational data is specially integrated into input features. A secondary decomposition and recombination (SDR) strategy is presented to reduce data non-stationarity. A dual-stage attention-based sequence-to-sequence (DA-Seq2Seq) model is employed to adaptively select the most relevant features at each time step, as well as capture long-term temporal dependencies. This approach is compared with baseline models on a dataset from a dry bulk port in northern China. The results reveal the advantages of SDR strategy and integrating operational data and show that this approach has higher accuracy than baseline models. The proposed approach can mitigate adverse effects of dust pollution from dry bulk ports on urban residents and help port authorities control dust pollution.

Ports and their influence on local air pollution and public health: A global analysis

Despite the skyrocketing growth in recent decades of environmental studies on ports and shipping, their local health impacts remain largely under-researched. This article tackles this gap in research by statistically analyzing data on global shipping flows across nearly 5000 ports in 35 OECD countries between 2001 and 2018. The different traffic types, from containers to bulk and passengers, are analyzed jointly with data on natural conditions, air pollution, socio-economic indicators, and public health. The principal results show that port regions pollute more than non-port regions on average, while health impacts vary according to the size and specialization of the port region. Three types of port regions are clearly differentiated: industrial, intermediate, and metropolitan port regions.

A Study on the Framework for Estimating Ship Air Pollutant Emissions—Focusing on Ports of South Korea

With the globalization of trade and the rapid development of the world economy, the problem of air pollution emissions produced by shipping is becoming more serious. The exhaust gas emitted by ships has become a significant source of air pollution in ocean and coastal areas. In recent years, governments have paid more attention to shipping emissions as a major source of environmental problems. Establishing ship emission inventories plays an important role in formulating ship emission control measures and regulations. This study aimed to propose a framework for calculating ship air pollutant emissions by comprehensively considering processes and methods officially used in developed countries such as the US and those in the EU, as well as South Korean circumstances and available data sets. The framework was divided into three sections: defining the inventory, data collection and analysis of the data, and ship air pollutant emission estimation. The results of this study provided a standard for South Korean domestic port emission inventories. A case study focused on the Gwangyang and Yeosu Ports, one of the leading port areas in South Korea, using adaptive data collection and emission-calculation processes. This study can be used as guidelines when the Ministry of Oceans and Fisheries (MOF) or the Ministry of Environment (MOE) adopts a standard process in South Korea in the near future. Subsequently, it is necessary to establish a national port emission management system to respond to world environmental changes.

ZIP Code-Level Estimation of Air Quality and Health Risk Due to Particulate Matter Pollution in New York City

Exposure to PM_2.5 is associated with hundreds of premature mortalities every year in New York City (NYC). Current air quality and health impact assessment tools provide county-wide estimates but are inadequate for assessing health benefits at neighborhood scales, especially for evaluating policy options related to energy efficiency or climate goals. We developed a new ZIP Code-Level Air Pollution Policy Assessment (ZAPPA) tool for NYC by integrating two reduced form models─Community Air Quality Tools (C-TOOLS) and the Co-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA)─that propagate emissions changes to estimate air pollution exposures and health benefits. ZAPPA leverages custom higher resolution inputs for emissions, health incidences, and population. It, then, enables rapid policy evaluation with localized ZIP code tabulation area (ZCTA)-level analysis of potential health and monetary benefits stemming from air quality management decisions. We evaluated the modeled 2016 PM_2.5 values against observed values at EPA and NYCCAS monitors, finding good model performance (FAC2, 1; NMSE, 0.05). We, then, applied ZAPPA to assess PM_2.5 reduction-related health benefits from five illustrative policy scenarios in NYC focused on (1) commercial cooking, (2) residential and commercial building fuel regulations, (3) fleet electrification, (4) congestion pricing in Manhattan, and (5) these four combined as a "citywide sustainable policy implementation" scenario. The citywide scenario estimates an average reduction in PM_2.5 of 0.9 μg/m³. This change translates to avoiding 210-475 deaths, 340 asthma emergency department visits, and monetized health benefits worth $2B to $5B annually, with significant variation across NYC's 192 ZCTAs. ZCTA-level assessments can help prioritize interventions in neighborhoods that would see the most health benefits from air pollution reduction. ZAPPA can provide quantitative insights on health and monetary benefits for future sustainability policy development in NYC.

Improving emissions inputs via mobile measurements to estimate fine-scale Black Carbon monthly concentrations through geostatistical space-time data fusion

Isolating air pollution sources in a complex transportation environment to quantify their contribution is challenging, particularly with sparse stationary measurements. Mobile measurements can add finer spatial resolution to support source apportionment, but they exhibit limitations when characterizing long term concentrations. Dispersion models can help overcome these limitations. However, they are only as reliable as their input emissions inventories. Herein, we developed an innovative method to revise emissions through inverse modeling and improve dispersion modeling predictions using stationary/mobile measurements. One specific revision estimated an adjustment factor of ~306 for warehouse emissions, indicating a significant underestimation of our initial estimates. This revised emission rate scaled up nationally would correspond to ~3.5% of the total Black Carbon emissions in the U.S. Nevertheless, domain-specific revisions only contribute to a 4% increase of area source emissions while improving R² from monthly estimates at fixed sites by 38%. After revising emissions through inverse dispersion modeling, we combine this model with stationary/mobile measurements through Bayesian Maximum Entropy (I-DISP BME) to produce temporally coarse yet spatially fine data fusion. We compare this novel data fusion approach to BME using only measurements (Flat BME). A 10-fold conventional cross-validation (representative of months with mobile measurements) shows that all BME methods have R² values that range from 0.787 to 0.798. A 2-fold cross-validation (representative of months with no mobile measurements) shows that the R² for I-DISP BME increases by a factor 90 when compared to Flat BME. Furthermore, not only is our novel I-DISP BME method more accurate than the classic Flat BME method, but the area it detects as highly exposed can be up to 5 times larger than that detected by the less accurate Flat BME method.

Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study

We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO₂; NO2-B43F), and oxidizing gases (OX-B431)—which refers to ozone (O₃) and NO₂. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R² measures (CO: RMSE = 18 ppb, R² = 0.97; NO: RMSE = 2 ppb, R² = 0.97). Performance measures for NO₂ and O₃ were somewhat lower (NO₂: RMSE = 3 ppb, R² = 0.79; O₃: RMSE = 4 ppb, R² = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.

Environmental Risk Assessment for PM2.5 Pollution from Non-Point Sources in the Mining Area Based on Multi-Source Superposition and Diffusion

To identify high-concentration contributing sources and highly dispersive pollution sources of fine particulate matter, analyze the relationship between the location and distribution shape of emission sources and the concentration contribution and dispersion of particulate matter, and realize the atmospheric environment risk simulation and the differential control of non-point sources in the mining area, taking a large mining area in Inner Mongolia as an example, we classified the emission sources of PM2.5 (particulate matter less than 2.5 μm) and complied with the emission inventory. A CALPUFF model was used to simulate the contribution for the PM2.5 concentration of six types of emission sources and a multi-source superposition. Through scenario simulation, we analyzed the relationship between the spatial distribution of emission sources and the emission concentration and diffusion in a large mining area. We analyzed the relative risks of six types of sources under the influence of other superimposed sources and the change of emission concentration during transmission. We established a three-dimensional evaluation model to assess the atmospheric environmental risk of PM2.5 non-point sources in the mining area, considering the change rate of PM2.5 concentration with migration, the relative contribution ratio of superimposed sources, and the equal contribution index of the standard concentration. The results show that the maximum equal contribution index of standard concentration of multi-source superposition was 4.40. Among them, non-paved roads, exposed surface sources of coal piles, and exposed surface sources of mine pits and dumps were the top three pollution contributors, and their maximum equal contribution indexes of standard concentration were 2.40, 2.21, and 2.10, respectively. The effect of superimposed pollution sources was affected by the wind field and the spatial distribution density of emission sources, while the dispersion was affected by the wind direction and the distribution direction of pollution sources. In the case of the same source intensity and emission area, the denser the source distribution was, the higher the emission concentration was, the smaller the contribution ratio of superimposed sources was, and the greater the relative pollution risk was. When the angle between the direction of dispersed linear pollution sources and the dominant wind direction was smaller, the emission concentration was higher, but the diffusion surface was smaller. When the angle with the direction of wind direction was larger, the emission concentration was lower, but the diffusion surface was larger. Concentrated pollution sources had the highest concentration and the diffusion surface was in the middle. Non-paved roads had the highest environmental risk, with an average risk of 5.61 × 10−2, followed by coal piles with an average value of 2.06 × 10−2, followed by pits and dumps with an average value of 1.89 × 10−2; the environmental risk of loading and unloading sources was the lowest. Unpaved roads were pollution sources with high relative pollution risk and diffusion risk, and their average relative pollution risk and diffusion risk were 2.34 × 10−2 and 3.28 × 10−2, respectively. In the case of multi-source superposition, the high-risk areas were mainly heavily polluted areas with intensive emission sources, while the medium-risk areas were moderately polluted areas with scattered pollution sources, and the diffusion risk was high. This research concludes that the dispersed distribution of pollution sources can reduce pollution risk, and the smaller the angle is between the linear distribution direction of pollution sources and the dominant wind direction, the smaller the diffusion risk is. Therefore, differentiated control can be carried out according to the characteristics of pollution sources. The conclusions can provide methods and theoretical support for the control of atmospheric environment risk, pollution prevention, and control planning in mining areas.

Shipping and Air Quality in Italian Port Cities: State-of-the-Art Analysis of Available Results of Estimated Impacts

Populated coastal areas are exposed to emissions from harbour-related activities (ship traffic, loading/unloading, and internal vehicular traffic), posing public health issues and environmental pressures on climate. Due to the strategic geographical position of Italy and the high number of ports along coastlines, an increasing concern about maritime emissions from Italian harbours has been made explicit in the EU and IMO (International Maritime Organization, London, UK) agenda, also supporting the inclusion in a potential Mediterranean emission control area (MedECA). This work reviews the main available outcomes concerning shipping (and harbours’) contributions to local air quality, particularly in terms of concentration of particulate matter (PM) and gaseous pollutants (mainly nitrogen and sulphur oxides), in the main Italian hubs. Maritime emissions from literature and disaggregated emission inventories are discussed. Furthermore, estimated impacts to air quality, obtained with dispersion and receptor modeling approaches, which are the most commonly applied methodologies, are discussed. Results show a certain variability that suggests the necessity of harmonization among methods and input data in order to compare results. The analysis gives a picture of the effects of this pollution source, which could be useful for implementing effective mitigation strategies at a national level.

Quantification of the impact of port activities on PM10 levels at the port-city boundary of a mediterranean city

The main objective of this work was to quantify the impact of handling of bulk materials on PM₁₀ levels measured at the port-city border of Alicante (Spain), located on the western Mediterranean coast. To achieve that goal, 355 PM₁₀ samples were collected at the perimeter of the harbor of Alicante from March 2017 to February 2018. A 181 sample subgroup was chemically characterized in order to perform a source apportionment study with the EPA PMF 5.0 model. Eight factors were identified, two of them directly related to the handling of bulk materials (Limestone + gypsum and Clinker), accounting jointly for 35% of the average PM₁₀ concentration. A Road traffic factor was the second highest contributor to PM₁₀ levels (17%) while the Shipping emissions factor accounted for only 6% of the average PM₁₀ mass. Other factors such as Biomass burning+ secondary nitrate and Aged sea salt represented a joint contribution of 25% of the PM₁₀ mass. Results indicate that emission abatement strategies should primarily focus on the reduction of fugitive emissions caused by the handling of bulk materials at the docks. Moreover, scenarios including reductions of more than 50% in bulk handling sources and 10% in other anthropogenic sources would help to reduce anthropogenic exceedances of the daily PM₁₀ limit (50 μg·m^-3) and to approach to WHO daily PM₁₀ standard (20 μg m^-3).

Comparison of the impact of ships to size-segregated particle concentrations in two harbour cities of northern Adriatic Sea

Detailed information on in-harbour shipping contribution to size segregated particles in coastal cities are scarce, especially in the busy Mediterranean basin. This poses issues for human exposure and air quality in urban harbour agglomerates, where only criteria pollutants (i.e. PM₁₀ and/or PM_2.5) are usually monitored. In this work, particle number and mass size distributions, in a large size range (0.01-31 μm), were obtained in two coastal cities of northern Adriatic Sea: Venice (Italy) and Rijeka (Croatia). Three size ranges were investigated: nanoparticles (diameter D < 0.25 μm); fine particles (0.25<D < 1 μm), and coarse particles (D > 1 μm). Absolute concentrations were larger in Venice for all size ranges showing, using analysis of daily trends, a large influence of local meteorology and boundary-layer dynamics. Contribution of road transport was larger (in relative terms) in Rijeka compared to Venice. The highest contributions of shipping were in Venice, mainly because of the larger ship traffic. Maximum impact was on nanoparticles 7.4% (Venice) and 1.8% (Rijeka), the minimum was on fine range 1.9% (Venice) and <0.2% (Rijeka) and intermediate values were found in the coarse fraction 1.8% (Venice) and 0.5% (Rijeka). Contribution of shipping to mass concentration was not distinguishable from uncertainty in Rijeka (<0.2% for PM₁, PM_2.5, and PM₁₀) and was about 2% in Venice. Relative contributions as function of particles size show remarkable similitudes: a maximum for nanoparticles, a quick decrease and a successive secondary maximum (2-3 times lower than the first) in the fine range. For larger diameters, the relative contributions reach a minimum at 1-1.5 μm and there is a successive increase in the coarse range. Size distributions showed a not negligible contribution of harbour emissions to nanoparticle and fine particle number concentrations, compared to PM_2.5 or PM₁₀, indicating them as a better metric to monitor shipping impacts compared to mass concentrations (PM_2.5 or PM₁₀).

Time-Frequency Analysis of Particulate Matter (PM10) Concentration in Dry Bulk Ports Using the Hilbert-Huang Transform

To analyze the time–frequency characteristics of the particulate matter (PM₁₀) concentration, data series measured at dry bulk ports were used to determine the contribution of various factors during different periods to the PM₁₀ concentration level so as to support the formulation of air quality improvement plans around port areas. In this study, the Hilbert–Huang transform (HHT) method was used to analyze the time–frequency characteristics of the PM₁₀ concentration data series measured at three different sites at the Xinglong Port of Zhenjiang, China, over three months. The HHT method consists of two main stages, namely, empirical mode decomposition (EMD) and Hilbert spectrum analysis (HSA), where the EMD technique is used to pre-process the HSA in order to determine the intrinsic mode function (IMF) components of the raw data series. The results show that the periods of the IMF components exhibit significant differences, and the short-period IMF component provides a modest contribution to all IMF components. Using HSA technology for these IMF components, we discovered that the variations in the amplitude of the PM₁₀ concentration over time and frequency are discrete, and the range of this variation is mainly concentrated in the low-frequency band. We inferred that long-term influencing factors determine the PM₁₀ concentration level in the port, and short-term influencing factors determine the difference in concentration data at different sites. Therefore, when formulating PM₁₀ emission mitigation strategies, targeted measures must be implemented according to the period of the different influencing factors. The results of this study can help guide recommendations for port authorities when formulating the optimal layout of measurement devices.

Investigation of CO2 Variation and Mapping Through Wearable Sensing Techniques for Measuring Pedestrians’ Exposure in Urban Areas

Citizens’ wellbeing is mainly threatened by poor air quality and local overheating due to human-activity concentration and land-cover/surface modification in urban areas. Peculiar morphology and metabolism of urban areas lead to the well-known urban-heat-island effect, characterized by higher air temperature in cities than in their surroundings. The environmental mapping of the urban outdoors at the pedestrian height could be a key tool to identify risky areas for humans in terms of both poor-air-quality exposure and thermal comfort. This study proposes urban environment investigation through a wearable miniaturized weather station to get the spatial distribution of key parameters according to the citizens’ perspective. The innovative system monitors and traces the field values of carbon dioxide (CO2) concentration, such as air temperature and wind-speed values, which have been demonstrated to be related to outdoor wellbeing. The presented monitoring campaign focused on a two-way, two-lane road in Rome (Italy) during traffic rush hours on both working days and weekends. Collected data were analyzed with respect to timing and position, and possible correlations among different variables were examined. Results demonstrated the wearable system capability to catch pedestrian-exposure variability in terms of CO2 concentration and local overheating due to urban structure, highlighting potentials in the citizens’ involvement as observation vectors to extensively monitor urban environmental quality.

Estimated health impacts from maritime transport in the Mediterranean region and benefits from the use of cleaner fuels

Ship traffic emissions degrade air quality in coastal areas and contribute to climate impacts globally. The estimated health burden of exposure to shipping emissions in coastal areas may inform policy makers as they seek to reduce exposure and associated potential health impacts. This work estimates the PM2.5-attributable impacts in the form of premature mortality and cardiovascular and respiratory hospital admissions, from long-term exposure to shipping emissions. Health impact assessment (HIA) was performed in 8 Mediterranean coastal cities, using a baseline conditions from the literature and a policy case accounting for the MARPOL Annex VI rules requiring cleaner fuels in 2020. Input data were (a) shipping contributions to ambient PM2.5 concentrations based on receptor modelling studies found in the literature, (b) population and health incidence data from national statistical registries, and (c) geographically-relevant concentration-response functions from the literature. Long-term exposure to ship-sourced PM2.5 accounted for 430 (95% CI: 220-650) premature deaths per year, in the 8 cities, distributed between groups of cities: Barcelona and Athens, with >100 premature deaths/year, and Nicosia, Brindisi, Genoa, Venice, Msida and Melilla, with tens of premature deaths/year. The more stringent standards in 2020 would reduce the number of PM2.5-attributable premature deaths by 15% on average. HIA provided a comparative assessment of the health burden of shipping emissions across Mediterranean coastal cities, which may provide decision support for urban planning with a special focus on harbour areas, and in view of the reduction in sulphur content of marine fuels due to MARPOL Annex VI in 2020.

Occurrence and risk assessment of benzothiazole, benzotriazole and benzenesulfonamide derivatives in airborne particulate matter from an industrial area in Spain

In this study we monitored benzothiazole (BTHs), benzotriazole (BTRs) and benzenesulfonamide (BSAs) derivatives in airborne particulate matter from four sampling sites near the port of Tarragona (Spain) over a one-year period. To do so, we developed a method based on ultrasound-assisted solvent extraction (USAE) followed by gas chromatography-mass spectrometry (GC-MS). We also studied concentrations of NO₂ and airborne particulate matter (PM_2.5 and PM_coarse) for a year. Our results showed NO₂ and PM_2.5 concentrations below the maximum average values established by the Europen Directive 2008/50/EC in the zone under study. Moreover, NO₂ values are directly proportional to changes in weather conditions and traffic emissions, while PM_coarse and PM_2.5 concentrations do not follow a clear trend as these may be generated from multiple sources (loading and unloading activities and traffic emissions). Regarding BTHs, BTRs and BSAs concentrations in particulate matter, the compounds found at the highest concentrations were 1-H-benzothiazole, 2-methylbenzothiazole, 2-chlorobenzothiazole, 1-H-benzotriazole, 4-methyl-1-H-benzotriazole, 2-(methylthio)-benzothiazole, 5-methyl-1-H-benzotriazole and bromobenzenesulfonamide with average concentrations ranging from 0.19 to 1.54 ng m^-3 in PM_coarse and from 0.09 to 0.61 ng m^-3 in PM_2.5. The remaining compounds were below the method quantification limits (MQLs) or were undetected in the samples analysed. Health risk values associated with the inhalation of the studied compounds were between 1.80 × 10^-3 and 1.27 × 10^-2 in the worst-exposure scenario.

Impact of harbour activities on local air quality: A review

Several harbour activities cause negative environmental impacts in the harbours' surrounding areas, namely the degradation of air quality. This paper intends to comprehensively review the status of the air quality measured in harbour areas. The published studies show a limited number of available air quality monitoring data in harbours areas, mostly located in Europe (71%). Measured concentrations of the main air pollutants were compiled and intercompared, for different countries worldwide allowing a large spatial representativeness. The higher NO₂ and PM₁₀ concentrations were found in Europe - ranging between 12 and 107 μg/m³ and 2-50 μg/m³, respectively, while the higher concentrations of PM_2.5 were found in Asia (25-70 μg/m³). In addition, the lower levels of SO₂ monitored in recent years suggest that current mitigation strategies adopted across Europe were very efficient in promoting the reduction of SO₂ concentrations. Part of the reviewed studies also estimated the contributions from ship emissions to PM concentration through the application of source apportionment methods, with an average of 5-15%. In some specific harbour areas in Asia, ships can contribute up to 7-26% to the local fine particulate matter concentrations. This review confirms that emissions from the maritime transport sector should be considered as a significant source of particulate matter in harbour areas, since this pollutant concentrations are frequently exceeding the established standard legal limit values. Therefore, the results from this review boost the implementation of mitigation measures, aiming to reduce, in particular, particulate matter emissions.

A methodology for the design of an effective air quality monitoring network in port areas

The assessment of the impact of ship emissions is generally realised by a network of receptors at ground level inside the port area or in the nearby urban canopy. Another possibility is the use of dispersion models capable of providing maps of concentrations to the ground taking into account ship emissions and weather conditions. In this work traffic data of passengers ships in the port of Naples were used to estimate pollutant emissions starting from EMEP/EEA (European Environment Agency/European Monitoring and Evaluation Programme) methodology and real data of power engines. In this way, a hourly file of emission rates was produced and input to CALPUFF together with meteorological data. Then SO₂ concentrations at different heights (0-60 m) in correspondence of selected points within the port area were evaluated. Results are compared with data measured at ground level in monitoring campaigns showing how is possible to better identify and quantify the air pollution from ships in port by positioning the receptors inside the port area at different heights from ground-level. The results obtained give useful information for designing an optimum on-site air quality monitoring network able to quantify the emissions of pollutants due to naval traffic and to individuate the contribution of single ships or ships' categories.

Vertical Distribution of Particulates within the Near-Surface Layer of Dry Bulk Port and Influence Mechanism: A Case Study in China

Knowing the vertical distribution of ambient particulate matter (PM) will help port authorities choose the optimal dust-suppression measures to reduce PM concentrations. In this study, we used an unmanned aerial vehicle (UAV) to assess the vertical distribution (0–120 m altitude) of PM in a dry bulk port along the Yangtze River, China. Total suspended particulates (TSP), PM10, and PM2.5 concentrations at different altitudes were measured at seven sites representing different cargo-handling sites and a background site. Variations in results across sites make it not suitable to characterize the vertical distribution of PM concentration at this port using simple representative distributions. Bulk cargo particle size, fog cannon use, and porous fence all affected the vertical distribution of TSP concentrations but had only minor impacts on PM10 and PM2.5 concentrations. Optimizing porous fence layout according to weather conditions and cargo demand at port have the most potential for mitigating PM pollution related to port operation. As ground-based stations cannot fully measure vertical PM distributions, our methods and results represent an advance in assessing the impact of port activities on air quality and can be used to determine optimal dust-suppression measures for dry bulk ports.

Combining Dispersion Modeling and Monitoring Data for Community-Scale Air Quality Characterization

Spatially and temporally resolved air quality characterization is critical for community-scale exposure studies and for developing future air quality mitigation strategies. Monitoring-based assessments can characterize local air quality when enough monitors are deployed. However, modeling plays a vital role in furthering the understanding of the relative contributions of emissions sources impacting the community. In this study, we combine dispersion modeling and measurements from the Kansas City TRansportation local-scale Air Quality Study (KC-TRAQS) and use data fusion methods to characterize air quality. The KC-TRAQS study produced a rich dataset using both traditional and emerging measurement technologies. We used dispersion modeling to support field study design and analysis. In the study design phase, the presumptive placement of fixed monitoring sites and mobile monitoring routes have been corroborated using a research screening tool C-PORT to assess the spatial and temporal coverage relative to the entire study area extent. In the analysis phase, dispersion modeling was used in combination with observations to help interpret the KC-TRAQS data. We extended this work to use data fusion methods to combine observations from stationary, mobile measurements, and dispersion model estimates.