Characterization Of Air Quality Research Articles

Peroxyacetyl nitrate can be used as a comprehensive indicator of air pollution complex

With the acceleration of air cleaning activities in China, air pollution has entered a new stage characterized by seasonal interplay and predominance of fine particulate matter (PM2.5) and ozone (O3) pollutants. However, the differing peak seasons of these two pollution preclude the use of a unified indicator for air pollution complex. Given that peroxyacetyl nitrate (PAN) originates from secondary formation and persists under low-temperature conditions for extended periods, it is vital to determine whether its concentration can be used as an indicator to represent air pollution, not only in summer but also in winter. Here, PAN observational data from 2018 to 2022 for Beijing were analyzed. The results showed that during photochemical pollution events in summer, secondary formation of PAN was intense and highly correlated with O3 (R = 0.8), while during PM2.5 pollution events in winter, when the lifetime of PAN is extended due to the low temperature, the PAN concentration was highly consistent with the PM2.5 concentration (R = 0.9). As a result, the PAN concentration essentially exhibited consistency with both the seasonal trends in the exceedance of air pollution (R = 0.6) and the air quality index (R = 0.8). When the daily average concentration exceeds 0.5 and 0.9 ppb, the PAN concentration can be used as a complementary indicator of the occurrence of primary and secondary standard pollution, respectively. This study demonstrated the unique role of PAN as an indicator of air pollution complex, highlighting the comprehensive ability for air quality characterization and reducing the burden of atmospheric environment management.

Air Quality Characterization and Trend Analysis in a Brazilian Industrialized Metropolitan Area in the Period from 1995 to 2022

The Brazilian population grew approximately 9% from 2006 to 2016, and the number of deaths caused by air pollution increased by 14% in Brazil in the same period. Facing the lack of studies on air quality in the Metropolitan Area of Belo Horizonte (MABH)—the third most populous Brazilian metropolitan area—this study aimed to investigate the air quality and the trends of air pollutant concentrations in the MABH between 1995 and 2022, using data from the air quality monitoring network. The methodology consisted of checking MABHs air quality trends following the WHO air quality guidelines. The Mann–Kendall test was used to check statistically for the possibility of tendencies. The results showed a trend of stability in the concentrations of air pollutants in the MABH without any trend of improvement or worsening. However, the time series of the MABH exposed the challenge of ensuring better air quality that protects human health. Furthermore, the results reinforced the importance of focusing on pollutant sources and exposed the need for improvements in air quality management. Thus, it is essential to reverse the current dismantling scenario of the public environmental agencies in Brazil.

Generation of a Controlled Polluted Atmosphere for Vehicles’ Cabins Air Quality Characterization

Generation of a Controlled Polluted Atmosphere for Vehicles’ Cabins Air Quality Characterization

Characterization of Air Quality Monitoring Stations in North Region of Malaysia Using Chemometric Technique in Relation to Human Health Impacts

Air quality status plays a vital indicator towards environment, economy as well as human health impacts of a country. Therefore, characterizing the spatial air quality monitoring stations is critical as it can be grouped into clusters in order to minimize the numbers of stations. It could be more efficient towards human health monitoring by strengthening management and reducing costs. However, the assessment of air quality pattern requires multiple variables to be analysed. It becomes a multivariate problem that requires novel methodologies as agglomerative hierarchical cluster analysis (AHC) is used primarily to determine cluster pattern [1]. It becomes a multidimensional problem that necessitates unique approaches, known as hybrid cluster technique. Therefore, this research aimed to show the hybrid cluster framework in air quality monitoring stations in north region of Peninsular Malaysia in order to give a superior geographic cluster distribution with a distinct validation.
 
 The data set for all air quality monitoring stations in north region (14 stations) was obtained from the Department of Environment, Malaysia (DOE) for the years 2018 to 2019 (two years). Six air quality pollutants (PM10, PM2.5, SO2, NO2, O3 and CO) were involved in this study. Before clustering the data, chemometric techniques such as principal component analysis (PCA) was used to summarize the information content in huge data tables in order to gain a better understanding of the variables in order to reduce dimensionality. The AHC was then created using the PCA factor scores. The factor scores were employed in a discriminant analysis (DA) to verify the clusters [2].
 
 Results from PCA factor scores showed 12 out of 14 stations needed to be further investigated using AHC. High Polluted Region (HPR=three stations), Moderate Polluted Region (MPR= four stations) and Low Polluted Region (LPR=five stations) were established from AHC and have the same characteristics as shown in Figure 1. Each class was distinguished using discriminant analysis (DA) for verification. The researchers discovered that each class has its own set of variables. Based on the DA, the confusion matrix for the clusters was 87.66 % indicating a high percentage of correct classification obtained from the validated data set (Table 1).
 The framework presented here is a novel tool for identifying and categorising stations based on air quality pollutants. The hybrid cluster analysis technique used in this study can produce more precise pollutant distributions, which are useful in air pollution research. In addition, the study could help to improve the existing methodology for air quality assessment in a more simplified way and a better evaluation of the air quality status, and thus could become an alternative way for analysis of changes in air quality, especially in the absence or limited historical data, in response to a better and more sustainable indicator in air quality assessment and management.

Characterization of indoor and ambient air quality in modern commercial and recreational complex buildings in Hanoi

The paper provides new information on the correlation between indoor and ambient air quality in selected commercial and recreational buildings, representing a cross-section of metropolitan building complexes in Hanoi, Vietnam. Air sampling carried out simultaneously at fixed locations within the case study directed the varying behavior across different zones and thus highlighted the relationship between parking basements, commercial floors, and ambient air sites. The fractions of coarse and fine particles were analyzed to identify the associated emission sources on a short-term basis. The elevated 8-h average levels of CO in underground parking zones (11–22 mg/m3) were significantly higher than those measured on commercial floors (0.5–0.9 mg/m3) and relatively ambient air (0.8–1 mg/m3) and exceeded the recommended exposure limit of 10 mg/m3 in the corresponding guidelines. Short-term averages of SO2 mass concentration (112–225 μg/m3) remained below the limits of 500 μg/m3 concerning health exposure in existing standards. The correlation of daily NO2 patterns was analyzed accordingly in relation to the operation of the individual building, the central ventilation mechanism, and the penetration of outside air, which is usually conditioned by traffic emission. The mass concentrations of PM10 and PM2.5 measured outdoors (21–32 μg/m3 and 12–14 μg/m3) and in parking basements (28–67 μg/m3 and 15–27 μg/m3) were generally higher than those recorded indoors at ground levels (1–29 μg/m3 and 1–20 μg/m3), frequently reaching the reference exposure values (15 μg/m3 and 5 μg/m3) for public health concerns. The determined levels and profiles of benzene, toluene, ethylbenzene, (m + p)-xylene, and o-xylene corresponding to the relative ratios of T/B and X/E revealed the responsible emissions from gasoline-fueled vehicles in parking basements and the influence of associated local traffic.

Correlation model between PM2.5 and atmospheric variables in the city of Villavicencio

Introduction: This article is the product of the research "Monitoring and Characterization of air quality in the downtown area of Villavicencio", carried out from 2018 to 2020 in Villavicencio - Colombia. Problem: Air quality directly impacts the health of the people who breathe it. Currently Villavicencio does not have enough monitoring stations, nor are there strategies to improve air quality. Objective: The main objective is to collect and analyse atmospheric and air quality information, to determinate the relationship between atmospheric variables and particulate matter. Methodology: Quantitative and experimental research in stages, a number of samples to be taken were defined and a statistical treatment was carried out. Different instruments were used to collect the variables. Results: The linear model between particulate material and atmospheric variables was obtained. The correlation index of particulate material with atmospheric variables was determined. The results were compared with regulations in Colombia and other countries. Conclusion: The results are within the limit allowed by national regulations. Linear models of the relationship between environmental variables and particulate matter were obtained. Originality: There are few studies related to air quality in the region. With the results, improvement actions can be proposed to local administrations to mitigate the impact on air quality in Villavicencio. Limitations: Sample collection time was limited, the availability of equipment in the institution where the research work originated also affected the scope of the work carried out.

Indoor Air Quality Monitoring and Characterization of Airborne Workstations Pollutants within Detergent Production Plant.

The indoor air quality (IAQ) of five workstations within a detergent production unit was monitored. Particulate matter (PM) was measured using a gravitational settlement method, and later characterized. To ascertain the quality of indoor air within the workstations, which could directly or indirectly affect the health and performance of the workers, a physical inspection of the plant premises was undertaken. The mean value of the following air-quality parameters; particulate matter(PM2.5), particulate matter (PM10), formaldehyde (HCHO), volatile organic compounds (VOCs), carbon dioxide (CO2), temperature (T) and percent relative humidity (%RH) were obtained within the range of 24.5–48.5 µg/m3, 26.75–61.75 µg/m3, 0.0–0.012 mg/m3, 0.09–1.35 mg/m3, 1137–1265 ppm, 25.65–28.15 °C and 20.13–23.8%, respectively. Of the particulate matter components characterized, sodium oxide (Na2O)—25.30 mg/m3, aluminum oxide (Al2O3)—22.93 mg/m3, silicon dioxide (SiO2)—34.17 mg/m3, sulfur trioxide (SO3)—41.57 mg/m3, calcium oxide (CaO)—10.94 mg/m3 and iron III oxide (Fe2O3)—19.23 mg/m3, were of significance. These results, compared with international standards for industrial indoor air quality, suggest that indoor air contamination emanating from the chemicals used in production workstations is traced to the design of the plant structures and the activities carried out within the workstations.

Characterization of Ambient Air Quality in Selected Urban Areas in Uganda Using Low-Cost Sensing and Measurement Technologies.

Air pollution is prevalent in cities and urban centers in developing countries including sub-Saharan Africa, but ground monitoring data on local pollution remain inadequate, hindering effective mitigation. We employed low-cost sensing and measurement technologies to quantify pollution levels based on particulate matter (PM2.5), NO2, and O3 over a 6 month period for selected urban centers in three of the four macroregions in Uganda. PM2.5 diurnal profiles exhibited consistent patterns across all monitoring locations with higher pollution levels manifesting from 18:00 to 00:00 and from 06:00 to 09:00; while the periods from 00:00 to 05:00 and from 09:00 to 17:00 had the lowest levels. Daily PM2.5 varied widely between 34 and 107 μg/m3 over a 7 day period, well within unhealthy levels (55.5-150.4 μg/m3) for short-term exposure. The inconsistent daily trend are instructive for multiple pollutant assessment to aid specific policy initiatives. The results also show inverse relations between seasonal particulate levels and precipitation, that is, R (correlation coefficient) = -0.93 and -0.62 for Kampala and Wakiso, R = -0.49 and -0.44 for the Eastern region, and R = -0.65 and -0.96 for the Western region. NO2 monthly concentrations replicated PM2.5 spatial levels, whereas O3 exhibited inverse relations probably due to a higher retention time in less-urbanized environments. Both PM2.5 and NO2 correlated positively with the resident population. Our findings show significant spatiotemporal variations and exceedances of health guidelines by about 4-6 times across most study locations (with two exceptions) for longer-term exposure. This paper demonstrably highlights the practicability and potential of low-cost approaches for air quality monitoring, with strong prospects for citizen science. This paper also provides novel information regarding air pollution that is needed to improve control strategies for reducing exposures.

Spatial and Temporal Analysis of Impacts of Hurricane Florence on Criteria Air Pollutants and Air Toxics in Eastern North Carolina.

Natural and anthropogenic disasters are associated with air quality concerns due to the potential redistribution of pollutants in the environment. Our objective was to conduct a spatiotemporal analysis of air concentrations of benzene, toluene, ethylbenzne, and xylene (BTEX) and criteria air pollutants in North Carolina during and after Hurricane Florence. Three sampling campaigns were carried out immediately after the storm (September 2018) and at four-month intervals. BTEX were measured along major roads. Concurrent criteria air pollutant concentrations were predicted from modeling. Correlation between air pollutants and possible point sources was conducted using spatial regression. Exceedances of ambient air criteria were observed for benzene (in all sampling periods) and PM2.5 (mostly immediately after Florence). For both, there was an association between higher concentrations and fueling stations, particularly immediately after Florence. For other pollutants, concentrations were generally below levels of regulatory concern. Through characterization of air quality under both disaster and “normal” conditions, this study demonstrates spatial and temporal variation in air pollutants. We found that only benzene and PM2.5 were present at levels of potential concern, and there were localized increases immediately after the hurricane. These substances warrant particular attention in future disaster response research (DR2) investigations.

Monitoring Rainwater Properties and Outdoor Particulate Matter in a Former Steel Manufacturing City in Romania

Wet deposition is influencing air quality because air pollutants are washed away from the surrounding air. Consequently, particulate matter and associated compounds are transported in the rainwater and enter into soil, surface waters, and groundwater. Nonpoint sources of heavy metals from stormwater runoff have increased in urban areas due to industrialization and the increasing impervious surfaces. In this work, we present an assessment of the rainwater composition regarding the nutrients and other physicochemical characteristics measured in three locations selected in Targoviste city, Romania, a city that had a specialized steel factory and important metallurgical facilities. The rainwater was collected using three PALMEX rain samplers and then was transferred to high-density polyethylene bottles and analyzed using ICP-MS. PM2.5 concentrations were also monitored continuously using optical monitors calibrated using a gravimetric sampler. A detailed analysis of the heavy metals content in rainwater and PM was presented for the pollution episodes occurring in October and November 2019. Backward trajectories were computed using the HYSPLIT model for these periods. The results showed that the PM2.5 ranged from 11.1 to 24.1 μg/m3 in 2019, while the heavy metals in collected rainwater were (µg L−1): 0.25 (Cd) − CV = 26.5%, 0.10 (Co) − CV = 58.1%, 1.77 (Cr) − CV = 24.3%, 377.37 (Ni) − CV = 27.9%, 0.67 (Pb) − CV = 74.3%, and 846.5 (Zn) − CV = 20.6%. Overall, Ni, Pb, Cr, and V had significant correlations between the concentrations from rainwater and PM. Negative associations were found between precipitation events and heavy metals both from rainwater and PM, but only a few showed statistical significance. However, this could explain the “washing” effect of the rain on the heavy metals from PM2.5. The potential sources of nitrogen in the rainwater collected in Targoviste could be from burning fossil fuels and the soils, including both biological processes and fertilization resulting from the intensive agriculture in the piedmont plain in which the city is located. Based on the results, rainwater monitoring can constitute a reliable method for air quality characterization. Additional research is required to better understand seasonality and sources of heterogeneity regarding the associations between PM and rainwater composition.

From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors

Low-cost sensors for particulate matter mass (PM) enable spatially dense, high temporal resolution measurements of air quality that traditional reference monitoring cannot. Low-cost PM sensors are especially beneficial in low and middle-income countries where few, if any, reference grade measurements exist and in areas where the concentration fields of air pollutants have significant spatial gradients. Unfortunately, low-cost PM sensors also come with a number of challenges that must be addressed if their data products are to be used for anything more than a qualitative characterization of air quality. The various PM sensors used in low-cost monitors are all subject to biases and calibration dependencies, corrections for which range from relatively straightforward (e.g. meteorology, age of sensor) to complex (e.g. aerosol source, composition, refractive index). The methods for correcting and calibrating these biases and dependencies that have been used in the literature likewise range from simple linear and quadratic models to complex machine learning algorithms. Here we review the needs and challenges when trying to get high-quality data from low-cost sensors. We also present a set of best practices to follow to obtain high-quality data from these low-cost sensors.

Bioburden in sleeping environments from Portuguese dwellings.

A wider characterization of indoor air quality during sleep is still lacking in the literature. This study intends to assess bioburden before and after sleeping periods in Portuguese dwellings through active methods (air sampling) coupled with passive methods, such as electrostatic dust cloths (EDC); and investigate associations between before and after sleeping and bioburden. In addition, and driven by the lack of information regarding fungi azole-resistance in Portuguese dwellings, a screening with supplemented media was also performed. The most prevalent genera of airborne bacteria identified in the indoor air of the bedrooms were Micrococcus (41%), Staphylococcus (15%) and Neisseria (9%). The major indoor bacterial species isolated in all ten studied bedrooms were Micrococcus luteus (30%), Staphylococcus aureus (13%) and Micrococcus varians (11%). Our results highlight that our bodies are the source of the majority of the bacteria found in the indoor air of our homes. Regarding air fungal contamination, Chrysosporium spp. presented the highest prevalence both in after the sleeping period (40.8%) and before the sleeping period (28.8%) followed by Penicillium spp. (23.47% morning; 23.6% night) and Chrysonilia spp. (12.4% morning; 20.3% night). Several Aspergillus sections were identified in air and EDC samples. However, none of the fungal species/strains (Aspergillus sections Fumigati, Flavi, Nidulantes and Circumdati) were amplified by qPCR in the analyzed EDC. The correlations observed suggest reduced susceptibility to antifungal drugs of some fungal species found in sleeping environments. Toxigenic fungal species and indicators of harmful fungal contamination were observed in sleeping environments.

A Mixed Eulerian–Lagrangian scheme for scalar transport

The tracking of pollutants in gas and liquid is a major problem to address in atmospheric environment, air quality characterization, and industrial material processes. A Lagrangian scheme devoted to the approximation of the advection term in an advection–diffusion equation is proposed to deal with small diffusivity values or large Peclet numbers. The Lagrangian scheme is used in practice as an Eulerian method discretized on Lagrangian marker points. Advection and diffusion of a circular concentration spot in a vortex flow are considered for validation purpose. The resulting mixed Eulerian–Lagrangian scheme reduces the numerical diffusion to almost computer error and provides better results than other Eulerian classical schemes of the literature. The scheme is finally illustrated in a natural convection situation.

Air Quality Characterization at Three Industrial Areas in Southern Italy

Outdoor air pollution is responsible for more than 4 million premature deaths worldwide and its contribution is particularly severe in industrial contaminated sites, where epidemiological studies highlight often mortality rate larger than the national average. In the framework of the CISAS project, this study investigates spatial and temporal variability of air pollution across three industrial contaminated sites in southern Italy, classified as “High Risk Area of Environmental Crisis”: Crotone, Milazzo and Priolo. The environmental analysis employed three investigation approaches: - meteorological characterization of the sites with analyses of local air mass circulation; - statistical evaluation of the continuous measurements of gaseous pollutants and PM concentration; - determination of mass concentration and detailed speciation of the chemical components of atmospheric particulate matter during intensive field campaigns. Continuous trace gases and aerosol measurements (including NOx, SO2, O3, NMHC, PM10 and PM2.5) over the period 2016-2018 were analysed, and specific intensive field experiments (2016-2017), representative of winter and summer conditions, were carried out in order to determine PM10 and PM2.5 chemical composition. The analyses of PM components (ions, elemental composition, trace organic pollutants, organic carbon, and elemental carbon) show concentrations typical of rural and urban areas. The results concerning gaseous pollutants and PM concentration showed a general compliance of the concentrations of some regulated species with the limits set by the EU Ambient Air Quality (AAQ) Directive. However, in particular in the industrial areas of Milazzo and Priolo, the analysis here reported highlights the need for a stringent regulation on NMHC ambient concentration and composition, further investigation of fine particle composition and atmospheric processing, and a deeper understanding of the role of anthropogenic emissions on ozone formation, also considering the World Health Organization (WHO) limits.

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.

Sensing of Indoor Air Quality—Characterization of Spatial and Temporal Pollutant Evolution Through Distributed Sensing

Discouraged by the high-cost and lack of connectivity of indoor air quality (iAQ) measurement equipment, we built a platform that would allow us to investigate what kinds of iAQ evolution information could be collected by a low-cost, distributed sensor network. Our platform measures a variety of iAQ metrics (CO¬2, HCHO, VOC, NO2, O3, Temp., RH), can be flexibly powered by batteries or standard 5W power supplies, and is connected to an infrastructure that supports an arbitrary number of nodes that push data to the cloud and record it in real-time. Some of the sensors used in our nodes generate data in standard units (like ppm or °c), and others provide an analog signal that cannot be directly converted into standard units. To increase the relative precision of measurements taken by different nodes, we placed all 6 pairs of the nodes used in our deployments in the same environment, recorded how they reacted to changing iAQ, and developed calibration functions to synchronize their signals. We deployed the comparatively cross-calibrated nodes to two different buildings on Princeton University’s Campus; a fabrication shop and an office building. In both buildings, we placed nodes at key positions in the ventilation supply chain, providing us with the ability to monitor where indoor air pollutants were being introduced, and when they tended to be introduced – enabling us to monitor the evolution of pollutants temporally and spatially. We find that the occupied space of the fabrication shop of the first building and the open plan office of the second building have higher levels of volatile organic compounds (VOCs) than outside air, indicating that both building's ventilation system is unable to supply enough fresh air to that space to dilute VOCs generated inside. In the second building, we also find indications that other parameters are being driven by set-backs and occupancy. These first deployments demonstrate the ability of low-cost distributed iAQ sensor networks to help researchers identify where and when indoor air pollutants are introduced in buildings.

Constraining chemical transport PM2.5 modeling outputs using surface monitor measurements and satellite retrievals: application over the San Joaquin Valley.

Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM2.5, its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System’s Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources.Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275 m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM2.5 fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R2 and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM2.5, 0.88 and 0.65 for NO3-, 0.78 and 0.23 for SO42-, and 1.01 for NH+, 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM2.5 improve by 30 % and 13 %, respectively, in comparison to unconstrained CTM simulations and provide finer spatial resolution. SO42- cross-validation values showed the largest spatial and spatiotemporal R2 improvement, with a 43 % increase. Assessing this physical technique in a well- instrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent.

Assessing the associations of daily respiratory symptoms and lung function in schoolchildren using an Air Quality Index for ozone: Results from the RESPOZE panel study in Athens, Greece

BackgroundAir Quality indicators or indices (AQIs) are mainly used for communicating the air pollution levels and risk to the general population. However, very few epidemiological studies have used AQIs for characterizing exposure. ObjectiveIn the framework of the RESPOZE panel study we evaluated the association of daily ozone AQI levels with the daily occurrence of respiratory symptoms and Peak Expiratory Flow (PEF) and compared the effects with those estimated using measurements from fixed outdoor monitoring sites, in the city of Athens, Greece. Materials and methodsA panel of 97 children, aged 10–11years, was followed intensively for 35days (5weeks) during the academic year 2013–14. PEF and symptoms were recorded daily by each child. Two ozone AQIs classifying the air quality into 7 categories of increasing severity, were calculated; one characterizing the whole Athens area and one the local area around the child's residence and school. Measurements from fixed sites were also used. Mixed effects models for repeated measurements were applied, adjusting for several confounders. ResultsIncreasing ozone levels were associated with increased incidence of symptoms, but the strongest and most statistically significant associations were found with the local air quality characterization with the AQI. Specifically, an increase in AQI-local by one category was associated with 34% (95% CI: 9%, 64%) increased odds of stuffy nose. When the AQI categories were “Bad” and “Severe”, an increase in the incidence of cough was observed (OR 3.05 (95% CI: 1.29, 7.22) and 6.42 (95% CI: 1.47, 28.03) respectively). We did not observe a statistically significant association between AQI and PEF. ConclusionOur results show that the use of an AQI based on local conditions may be advantageous over the use of only measurements when investigating the effects of air pollution on health outcomes for improving communication of risk to the public.

Spatial and Temporal Dynamics in Air Pollution Exposure Assessment.

Analyzing individual exposure in urban areas offers several challenges where both the individual’s activities and air pollution levels demonstrate a large degree of spatial and temporal dynamics. This review article discusses the concepts, key elements, current developments in assessing personal exposure to urban air pollution (seventy-two studies reviewed) and respective advantages and disadvantages. A new conceptual structure to organize personal exposure assessment methods is proposed according to two classification criteria: (i) spatial-temporal variations of individuals’ activities (point-fixed or trajectory based) and (ii) characterization of air quality (variable or uniform). This review suggests that the spatial and temporal variability of urban air pollution levels in combination with indoor exposures and individual’s time-activity patterns are key elements of personal exposure assessment. In the literature review, the majority of revised studies (44 studies) indicate that the trajectory based with variable air quality approach provides a promising framework for tackling the important question of inter- and intra-variability of individual exposure. However, future quantitative comparison between the different approaches should be performed, and the selection of the most appropriate approach for exposure quantification should take into account the purpose of the health study. This review provides a structured basis for the intercomparing of different methodologies and to make their advantages and limitations more transparent in addressing specific research objectives.

Characterization of aircraft emissions and air quality impacts of an international airport

Beijing Capital International Airport (ZBAA) is the world's second busiest airport. In this study, the emissions of air pollutants from aircraft and other sources at ZBAA in 2015 were estimated using an improved method, which considered the mixing layer height calculated based on aircraft meteorological data relay (AMDAR), instead of using the height (915m) recommended by ICAO. The yearly emissions of NOx, CO, VOCs, SO2, and PM2.5 at the airport were 8.76×103, 4.43×103, 5.43×102, 4.80×102, and 1.49×102ton/year, respectively. The spatial–temporal distribution of aircraft emissions was systematically analyzed to understand the emission characteristics of aircraft. The results indicated that NOx was mainly emitted during the take-off and climb phases, accounting for 20.5% and 55.5% of the total emissions. CO and HC were mainly emitted during the taxi phase, accounting for 91.6% and 92.2% of the total emissions. Because the mixing layer height was high in summer, the emissions of aircraft were at the highest level throughout the year. Based on the detailed emissions inventory, four seasons simulation using WRF-CMAQ model was performed over the domain surrounding the airport. The results indicated that the contribution to PM2.5 was relatively high in winter; the average impact was about 1.15μg/m3 within a radius of 1km around the airport. Meanwhile, the near surroundings and southwest areas of the airport are the most sensitive to PM2.5.