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Construction machinery (CM) is considered to be a significant source of air pollution. The estimation of emissions from CM is essential for policy-makers to control air pollution and reduce carbon emissions. In this study, air pollutant emissions from CM with four emissions standards (Beijing I-IV) in Beijing from 2015 to 2019 were estimated by combining data from the Non-road Mobile Source Emissions Inventory Compiled Technical Guidelines, Motor Vehicle Emission Simulator (MOVES) and actual measurements. Taking an example for 2019, emissions of hydrocarbons (HCs), carbon monoxide (CO), nitrogen oxides (NO x ), particulate matter (PM) and carbon dioxide (CO 2 ) were estimated to be 4.80 Gg, 16.51 Gg, 27.77 Gg, 1.35 Gg and 5.09 Tg, respectively, representing annual mean decreases of 13.2%, 13.1%, 10.8%, 15.2% and 3.5%, respectively, over the five-year period. Tongzhou, Shunyi and Changping Districts contributed 53-67% of total CM emissions in 2019. Among the ten types of CM considered, loaders were the largest contributors to total emissions, accounting for 41-54% of total CM emissions in Beijing from 2015 to 2019. Machinery with a mean power above 75 kW accounted for the largest share (67-78%) of total CM emissions in Beijing from 2015 to 2019. Our results contribute to the limited data of estimated CM emissions and can help develop control strategies to improve air quality and alleviate climate change., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

This study investigated temporal variability of ground-level ozone (O 3 ) during smog season in Windsor, Canada and surrounding states of the US during 1996-2015. Cluster analysis classified six sites into two groups with similar features of O 3 concentrations. The first group consists of four urban/suburban sites, Windsor, Allen Park and Lansing in Michigan, and Erie in Ohio, and the second group includes two rural sites, Delaware and National Trail School in Ohio. The similarities among all six sites include (1) diurnal and seasonal variability of O 3 concentrations owing to similar weather conditions, and (2) decreasing peak O 3 (95 th percentile) concentrations due to reduced emissions of precursors, thus less photochemical O 3 formation. However, how O 3 levels changed with reduced NO X emissions during the study period and on weekends differed between the two groups. Lower O 3 concentrations were recorded at urban/suburban sites (30 ppb) than at rural sites (34 ppb). At urban sites, annual smog season O 3 concentrations increased by 0.12-3.2 ppb/year. The increasing trends occurred at all percentile levels except for 95 th percentile and in most months, due to weakened NO titration effect. At the rural sites, smog-season O 3 concentrations decreased by 0.01-2.71 ppb/year. The decreasing trends were observed at 50-95 th percentile levels and in most months. Between the two groups, the urban/suburban group had a greater increment in weekend O 3 concentrations (3.3 vs. 1.6 ppb) due to a greater reduction of local NO emissions on weekend, thus, weakened NO titration effect. Overall, O 3 formation was more sensitive to VOCs during the study period; however, the O 3 formation regime gradually shifted toward more sensitive to NO X during 1996-2007 then became more sensitive to VOCs during 2008-2015. Therefore, controlling anthropogenic VOC emissions is needed to effectively mitigate O 3 pollution in the study region., (Crown Copyright © 2022. Published by Elsevier Ltd. All rights reserved.)

This study investigates spatial-temporal variability and trends of ambient PM 2.5 in Beijing, China, using data collected from eight urban and four suburban stations. During 2013-2018, the city-wide annual PM 2.5 concentrations decreased significantly by 40% (84 μg/m 3 in 2013 vs. 50 μg/m 3 in 2018). The decreasing PM 2.5 trend is more pronounced in winter and during the heating season (November-March), in urban areas, and at the median and upper percentiles of PM 2.5 concentrations. The 95th percentile PM 2.5 concentrations had decreased by 20 μg/m 3 /yr in the heating season and 16 μg/m 3 /yr in the non-heating season. During the six-year study period, there was a significant increase in excellent air quality days (PM 2.5 concentration < 35 μg/m 3 ) and a significant decrease in heavy pollution days (PM 2.5 concentration > 150 μg/m 3 ). PM 2.5 concentrations were strongly correlated across the 12 stations. Urban areas in south Beijing experienced higher PM 2.5 levels than suburban sites at every hour-of-day, day-of-week, and month-of-year. PM 2.5 levels were higher during winter and the heating season, when PM 2.5 emission was high due to space heating and mixing layer heights were low. PM 2.5 was higher at weekends than during weekdays, when 20% of private passenger vehicles are prohibited, and higher at night than during the day, when heavy duty delivery vehicles are not permitted. These temporal and spatial trends suggest that Beijing's PM 2.5 is strongly impacted by local emissions. Our results indicate, control strategies implemented were successful in Beijing's air quality improvement, but further reduction of PM 2.5 concentrations in Beijing could be challenging due to significant contribution from its neighboring cities, calling for comprehensive and collaborative efforts in regional/national scale., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The aim of this study was to assess how children's personal exposure to fine particulate matter (PM 2.5 ) during the school commute is influenced by mode of travel and neighborhood environment in a mid-sized Canadian city. A total of 101 commutes to and from school were tracked using a GPS, and personal exposure to PM 2.5 along commute routes was assessed by spatially-referencing the monitored exposure levels with time-synchronized GPS data. Students who walked to and from school were exposed to lower PM 2.5 concentrations than those in cars or riding the school bus. There was also a significant difference in mean PM 2.5 concentrations by the built environment, with children who walked to school in suburban neighborhoods experiencing higher personal concentrations than children in urban neighborhoods. To reduce children's daily exposure to air pollutants, neighborhoods should be designed to maximize the number of children who are able to walk between home and school., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Forty eight potential outliers in air pollution measurements taken simultaneously in Detroit, Michigan, USA and Windsor, Ontario, Canada in 2008 and 2009 were identified using four independent methods: box plots, variogram clouds, difference maps, and the Local Moran's I statistic. These methods were subsequently used in combination to reduce and select a final set of 13 outliers for nitrogen dioxide (NO 2 ), volatile organic compounds (VOCs), total benzene, toluene, ethyl benzene, and xylene (BTEX), and particulate matter in two size fractions (PM 2.5 and PM 10 ). The selected outliers were excluded from the measurement datasets and used to revise air pollution models. In addition, a set of temporally-scaled air pollution models was generated using time series measurements from community air quality monitors, with and without the selected outliers. The influence of outlier exclusion on associations with asthma exacerbation rates aggregated at a postal zone scale in both cities was evaluated. Results demonstrate that the inclusion or exclusion of outliers influences the strength of observed associations between intraurban air quality and asthma exacerbation in both cities. The box plot, variogram cloud, and difference map methods largely determined the final list of outliers, due to the high degree of conformity among their results. The Moran's I approach was not useful for outlier identification in the datasets studied. Removing outliers changed the spatial distribution of modeled concentration values and derivative exposure estimates averaged over postal zones. Overall, associations between air pollution and acute asthma exacerbation rates were weaker with outliers removed, but improved with the addition of temporal information. Decreases in statistically significant associations between air pollution and asthma resulted, in part, from smaller pollutant concentration ranges used for linear regression. Nevertheless, the practice of identifying outliers through congruence among multiple methods strengthens confidence in the analysis of outlier presence and influence in environmental datasets., (Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.)

Diesel particulate matter (DPM) is associated with adverse human health effects. This study aims to investigate the relationship between DPM exposure and emissions by estimating the individual intake fraction (iFi) and population intake fraction (iFp) of DPM. Daily average concentrations of particulate matter at two bus stops during rush hours were measured, and then they were apportioned to DPM due to heavy-duty diesel bus emissions using Chemical Mass Balance Model. The DPM emissions of diesel buses for different driving conditions (idling, creeping and traveling) were estimated on the basis of field observations and published emission factors. The median iFi of DPM was 0.67 and 1.39 per million for commuters standing at the bus stop and pedestrians/cyclists passing through the bus stop during rush hours, respectively. The median iFp of DPM was 94 per million. Estimations of iFi and iFp of DPM are potentially significant for exposure assessment and risk management., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Background: We examined the impact of data source and exposure measurement error for ambient NO2 on risk estimates derived from a case-crossover study of emergency room visits for asthma in Windsor, Canada between 2002 and 2009., Methods: Paired personal and fixed-site NO2 data were available from an independent population (47 children and 48 adults) in Windsor between 2005 and 2006. We used linear regression to estimate the relationship and measurement error variance induced between fixed site and personal measurements of NO2, and through a series of simulations, evaluated the potential for a Bayesian model to adjust for this change in scale and measurement error. Finally, we re-analyzed data from the previous case-crossover study adjusting for the estimated change in slope and measurement error., Results: Correlations between paired NO2 measurements were weak (R(2)≤0.08) and slopes were far from unity (0.0029≤β≤0.30). Adjusting the previous case-crossover analysis suggested a much stronger association between personal NO2 (per 1ppb) (Odds Ratio (OR)=1.276, 95% Credible Interval (CrI): 1.034, 1.569) and emergency room visits for asthma among children relative to the fixed-site estimate (OR=1.024, 95% CrI 1.004-1.045)., Conclusions: Our findings suggest that risk estimates based on fixed-site NO2 concentrations may differ substantially from estimates based on personal exposures if the change in scale and/or measurement error is large. In practice, one must always keep the scale being used in mind when interpreting risk estimates and not assume that coefficients for ambient concentrations reflect risks at the personal level., (Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.)

The Geospatial Determinants of Health Outcomes Consortium (GeoDHOC) study investigated ambient air quality across the international border between Detroit, Michigan, USA and Windsor, Ontario, Canada and its association with acute asthma events in 5- to 89-year-old residents of these cities. NO2, SO2, and volatile organic compounds (VOCs) were measured at 100 sites, and particulate matter (PM) and polycyclic aromatic hydrocarbons (PAHs) at 50 sites during two 2-week sampling periods in 2008 and 2009. Acute asthma event rates across neighborhoods in each city were calculated using emergency room visits and hospitalizations and standardized to the overall age and gender distribution of the population in the two cities combined. Results demonstrate that intra-urban air quality variations are related to adverse respiratory events in both cities. Annual 2008 asthma rates exhibited statistically significant positive correlations with total VOCs and total benzene, toluene, ethylbenzene and xylene (BTEX) at 5-digit zip code scale spatial resolution in Detroit. In Windsor, NO2, VOCs, and PM10 concentrations correlated positively with 2008 asthma rates at a similar 3-digit postal forward sortation area scale. The study is limited by its coarse temporal resolution (comparing relatively short term air quality measurements to annual asthma health data) and interpretation of findings is complicated by contrasts in population demographics and health-care delivery systems in Detroit and Windsor.

Annoyance produced by air pollution has been suggested as a useful proxy for determining ambient air pollution exposure. However, most of the studies, to date, have focused on nitrogen dioxide and sulphur dioxide, with no work done on volatile organic compounds (VOC). This study is aimed at examining the associations between odour annoyance and VOC in 'Chemical Valley', Sarnia, Ontario, Canada. Annoyance scores were extracted from a community health survey (N = 774), and exposures to VOC were estimated from respondents' six-digit alphanumeric postal codes using land use regression models. Univariate analyses were used to explore the relationships between odour annoyance and modelled pollutants, whilst multivariate ordinal logistic regression was utilized to examine the determinants of odour annoyance. The results indicate that odour annoyance is significantly associated with modelled benzene, toluene, ethylbenzene, o-xylene and (m + p) xylene (BTEX) pollutants. The findings also show that the determinants of odour annoyance in the context of VOC include gender, number of relatives in the community, perception of air pollution, community satisfaction, medical checkups, ability to cope with daily life demands and general symptoms. When compared, the analysis indicates that Sarnia residents respond to considerably lower BTEX concentrations than the allowable 'safe' levels in the province of Ontario. In general, the results exhibit a dose-response gradient with annoyance score increasing with rising modelled pollutant concentrations. The observed relationships suggest that odour annoyance might be a function of true exposure and may serve as a proxy for air quality and ambient air pollution monitoring. However, questionnaire-based odour annoyance scores need to be longitudinally validated across different geographical scales and pollutants if they are to be adopted at the national level.

Land use regression (LUR) models have been widely used to characterize the spatial distribution of urban air pollution and estimate exposure in epidemiologic studies. However, spatial patterns of air pollution vary greatly between cities due to local source type and distribution. London, Ontario, Canada, is a medium-sized city with relatively few and isolated industrial point sources, which allowed the study to focus on the contribution of different transportation sectors to urban air pollution. This study used LUR models to estimate the spatial distribution of nitrogen dioxide (NO2) and to identify local sources influencing NO2 concentrations in London, ON. Passive air sampling was conducted at 50 locations throughout London over a 2-week period in May-June 2010. NO2 concentrations at the monitored locations ranged from 2.8 to 8.9 ppb, with a median of 5.2 ppb. Industrial land use, dwelling density, distance to highway, traffic density, and length of railways were significant predictors of NO2 concentrations in the final LUR model, which explained 78% of NO2 variability in London. Traffic and dwelling density explained most of the variation in NO2 concentrations, which is consistent with LUR models developed in other Canadian cities. We also observed the importance of local characteristics. Specifically, 17% of the variation was explained by distance to highways, which included the impacts of heavily traveled corridors transecting the southern periphery of the city. Two large railway yards and railway lines throughout central areas of the city explained 9% of NO2 variability. These results confirm the importance of traditional LUR variables and highlight the importance of including a broader array of local sources in LUR modeling. Finally, future analyses will use the model developed in this study to investigate the association between ambient air pollution and cardiovascular disease outcomes, including plaque burden, cholesterol, and hypertension.

An innovative approach of mean emission by vehicle type was used in this paper to assess the impact of new vehicle emission standards in Beijing, China during the period of 2000-2005. It was found that CO and NO(x) emissions decreased by 48% and 23%, respectively, from Type O (before 2000) to Type I (year 2000) vehicles. The reductions from Type O to Type II (year 2002) vehicles were 85% and 73% for CO and NO(x), respectively. When all three types of vehicles (Types O, I and II) are combined, the annual per vehicle CO emissions decreased from 586 kg per vehicle per year in 2000 to 324 kg per vehicle per year in 2005, while that of NO(x) decreased from 66.9 to 43.4 kg per vehicle per year, which was mainly resulted from the impact of stringent new vehicle emission standards implemented in years 2000 and 2002. However, the vehicle population increased by 70% during the same time period, which offset the impact of cleaner vehicles. Thus, the total vehicle emission decreased little for CO (885,000 tons in 2000, 837,000 tons in 2005) and even increased slightly for NO(x) (101,000 and 112,000 tons in 2000 and 2005, respectively). The ambient concentrations of CO decreased significantly throughout 2000-2005, the same trend was not observed for NO(2). Correlation analysis (grey correlation and Pearson correlation) between the annual vehicle emissions and annual concentrations of CO, the annual NO(x) emission and annual NO(2) concentration indicated that the implementation of new vehicle emission standards was associated with the abatement of ambient CO and NO(2) concentrations in Beijing.

The Windsor, Ontario Exposure Assessment Study evaluated the contribution of ambient air pollutants to personal and indoor exposures of adults and asthmatic children living in Windsor, Ontario, Canada. In addition, the role of personal, indoor, and outdoor air pollution exposures upon asthmatic children's respiratory health was assessed. Several active and passive sampling methods were applied, or adapted, for personal, indoor, and outdoor residential monitoring of nitrogen dioxide, volatile organic compounds, particulate matter (PM; PM-2.5 pm [PM2.5] and < or =10 microm [PM10] in aerodynamic diameter), elemental carbon, ultrafine particles, ozone, air exchange rates, allergens in settled dust, and particulate-associated metals. Participants completed five consecutive days of monitoring during the winter and summer of 2005 and 2006. During 2006, in addition to undertaking the air pollution measurements, asthmatic children completed respiratory health measurements (including peak flow meter tests and exhaled breath condensate) and tracked respiratory symptoms in a diary. Extensive quality assurance and quality control steps were implemented, including the collocation of instruments at the National Air Pollution Surveillance site operated by Environment Canada and at the Michigan Department of Environmental Quality site in Allen Park, Detroit, MI. During field sampling, duplicate and blank samples were also completed and these data are reported. In total, 50 adults and 51 asthmatic children were recruited to participate, resulting in 922 participant days of data. When comparing the methods used in the study with standard reference methods, field blanks were low and bias was acceptable, with most methods being within 20% of reference methods. Duplicates were typically within less than 10% of each other, indicating that study results can be used with confidence. This paper covers study design, recruitment, methodology, time activity diary, surveys, and quality assurance and control results for the different methods used.

Continuous monitors can be used to supplement traditional filter-based methods of determining personal exposure to air pollutants. They have the advantages of being able to identify nearby sources and detect temporal changes on a time scale of a few minutes. The Windsor Ontario Exposure Assessment Study (WOEAS) adopted an approach of using multiple continuous monitors to measure indoor, outdoor (near-residential) and personal exposures to PM₂.₅, ultrafine particles and black carbon. About 48 adults and households were sampled for five consecutive 24-h periods in summer and winter 2005, and another 48 asthmatic children for five consecutive 24-h periods in summer and winter 2006. This article addresses the laboratory and field validation of these continuous monitors. A companion article (Wheeler et al., 2010) provides similar analyses for the 24-h integrated methods, as well as providing an overview of the objectives and study design. The four continuous monitors were the DustTrak (Model 8520, TSI, St. Paul, MN, USA) and personal DataRAM (pDR) (ThermoScientific, Waltham, MA, USA) for PM₂.₅; the P-Trak (Model 8525, TSI) for ultrafine particles; and the Aethalometer (AE-42, Magee Scientific, Berkeley, CA, USA) for black carbon (BC). All monitors were tested in multiple co-location studies involving as many as 16 monitors of a given type to determine their limits of detection as well as bias and precision. The effect of concentration and electronic drift on bias and precision were determined from both the collocated studies and the full field study. The effect of rapid changes in environmental conditions on switching an instrument from indoor to outdoor sampling was also studied. The use of multiple instruments for outdoor sampling was valuable in identifying occasional poor performance by one instrument and in better determining local contributions to the spatial variation of particulate pollution. Both the DustTrak and pDR were shown to be in reasonable agreement (R² of 90 and 70%, respectively) with the gravimetric PM₂.₅ method. Both instruments had limits of detection of about 5 μg/m³. The DustTrak and pDR had multiplicative biases of about 2.5 and 1.6, respectively, compared with the gravimetric samplers. However, their average bias-corrected precisions were <10%, indicating that a proper correction for bias would bring them into very good agreement with standard methods. Although no standard methods exist to establish the bias of the Aethalometer and P-Trak, the precision was within 20% for the Aethalometer and within 10% for the P-Trak. These findings suggest that all four instruments can supply useful information in environmental studies.

As part of a community health study, an air quality investigation was carried out in Sarnia, Ontario, Canada, which is located within a government-designated "Area of Concern". The Sarnia area, also known as Chemical Valley, has been known to have an abundance of air quality problems that are frequently attributed to the large number of chemical companies located in the area, which are among the largest industrial sources of criteria air pollutants in Canada. Ambient volatile organic compounds (VOC) were monitored during a 2-wk period in October 2005 at 37 sites across the city of Sarnia, using 3M organic vapor monitors. The spatial variability was investigated using ranking and mapping techniques to identify spatial patterns of the VOC across the city. The results show a large spatial variability of most of the VOC investigated. Furthermore, most of the VOC were highly correlated, suggesting common sources in Sarnia. The results also indicate that the industrial and chemical facilities strongly influence the concentrations of VOC in Sarnia, with higher outdoor VOC levels found closer to industrial facilities. The measured VOC and those obtained from a long-term government monitoring station were compared. It was determined that the exposure gradients observed during the study period are relatively representative of the long-term pattern. The results of this study will be used for the community health study.

There are acknowledged difficulties in epidemiological studies to accurately assign exposure to air pollution for large populations, and large, long-term cohort studies have typically relied upon data from central monitoring stations. This approach has generally been adequate when populations span large areas or diverse cities. However, when the effects of intra-urban differences in exposure are being studied, the use of these existing central sites are likely to be inadequate for representing spatial variability that exists within an urban area. As part of the Border Air Quality Strategy (BAQS), an international agreement between the governments of Canada and the United States, a number of air health effects studies are being undertaken by Health Canada and the US EPA. Health Canada's research largely focuses on the chronic exposure of elementary school children to air pollution. The exposure characterization for this population to a variety of air pollutants has been assessed using land-use regression (LUR) models. This approach has been applied in several cities to nitrogen dioxide (NO2), as an assumed traffic exposure marker. However, the models have largely been developed from limited periods of saturation monitoring data and often only represent one or two seasons. Two key questions from these previous efforts, which are examined in this paper, are: If NO2 is a traffic marker, what other pollutants, potentially traffic related, might it actually represent? How well is the within city spatial variability of NO2, and other traffic-related pollutants, characterized by a single saturation monitoring campaign. Input data for the models developed in this paper were obtained across a network of 54 monitoring sites situated across Windsor, Ontario. The pollutants studied were NO2, sulfur dioxide (SO2) and volatile organic compounds, which were measured in all four seasons by deploying passive samplers for 2-week periods. Correlations among these pollutants were calculated to assess what other pollutants NO2 might represent, and correlations across seasons for a given pollutant were determined to assess how much the within-city spatial pattern varies with time. LUR models were then developed for NO2, SO2, benzene, and toluene. A multiple regression model including proximity to the Ambassador Bridge (the main Canada-US border crossing point), and proximity to highways and major roads, predicted NO2 concentrations with an R2=0.77. The SO2 model predictors included distance to the Ambassador Bridge, dwelling density within 1500m, and Detroit-based SO2 emitters within 3000m resulting in a model with an R2=0.69. Benzene and toluene LUR models included traffic predictors as well as point source emitters resulting in R2=0.73 and 0.46, respectively. Between season pollutant correlations were all significant although actual concentrations for each site varied by season. This suggests that if one season were to be selected to represent the annual concentrations for a specific site this may lead to a potential under or overestimation in exposure, which could be significant for health research. All pollutants had strong inter-pollutant correlations suggesting that NO2 could represent SO2, benzene, and toluene.

This study aimed at developing models to predict nitrogen dioxide (NO(2)) and sulfur dioxide (SO(2)) concentrations in Sarnia, "Chemical Valley", Ontario, Canada, and model the intra-urban variation of ambient NO(2) and SO(2) in the city for a community health study. NO(2) and SO(2) samples were monitored with Ogawa passive samplers at 39 locations across the city for 2 wk during the fall of 2005. The final land use regression models were constructed to generate independent variables that might best predict NO(2) and SO(2) concentrations. The coefficients of determinations for the final NO(2) and SO(2) models were .79 and .66, respectively. The explanatory variables in the final NO(2) model were: proximity to the industrial core, industrial areas within 1600 m, highways within 400 m, and dwelling counts within 2400 m. The variables in the final SO(2) model were: proximity to the industrial core, industrial areas within 1200 m, and major roads within 100 m. The spatial variations captured in these analyses are being used to estimate ambient pollution concentrations for a large health study.

The objectives of this study were: (1) to quantify the errors associated with saturation air quality monitoring in estimating the long-term (i.e., annual and 5 yr) mean at a given site from four 2-week measurements, once per season; and (2) to develop a sampling strategy to guide the deployment of mobile air quality facilities for characterizing intraurban gradients of air pollutants, that is, to determine how often a given location should be visited to obtain relatively accurate estimates of the mean air pollutant concentrations. Computer simulations were conducted by randomly sampling ambient monitoring data collected in six Canadian cities at a variety of settings (e.g., population-based sites, near-roadway sites). The 5-yr (1998-2002) dataset consisted of hourly measurements of nitric oxide (NO), nitrogen dioxide (NO2), oxides of nitrogen (NOx), sulfur dioxide (SO2), coarse particulate matter (PM10), fine particulate matter (PM2.5), and CO. The strategy of randomly selecting one 2-week measurement per season to determine the annual or long-term average concentration yields estimates within 30% of the true value 95% of the time for NO2, PM10 and NOx. Larger errors, up to 50%, are expected for NO, SO2, PM2.5, and CO. Combining concentrations from 85 random 1-hr visits per season provides annual and 5-yr average estimates within 30% of the true value with good confidence. Overall, the magnitude of error in the estimates was strongly correlated with the variability of the pollutant. A better estimation can be expected for pollutants known to be less temporally variable and/or over geographic areas where concentrations are less variable. By using multiple sites located in different settings, the relationships determined for estimation error versus number of measurement periods used to determine long-term average are expected to realistically portray the true distribution. Thus, the results should be a good indication of the potential errors one could expect in a variety of different cities, particularly in more northern latitudes.

There have been several methods employed to quantify individual-level exposure to ambient traffic-related air pollutants (TRAP). These include an individual's residential proximity to roads, measurement of individual pollutants as surrogates or markers, as well as dispersion and land use regression (LUR) models. Hopanes are organic compounds still commonly found on ambient particulate matter and are specific markers of combustion engine primary emissions, but they have not been previously used in personal exposure studies. In this paper, children's personal exposures to TRAP were evaluated using hopanes determined from weekly integrated filters collected as part of a personal exposure study in Windsor, Canada. These hopane measurements were used to evaluate how well other commonly used proxies of exposure to TRAP performed. Several of the LUR exposure estimates for a range of air pollutants were associated with the children's summer personal hopane exposures (r = 0.41–0.74). However, all personal hopane exposures in summer were more strongly associated with the length of major roadways within 500 m of their homes. In contrast, metrics of major roadways and LUR estimates were poorly correlated with any winter personal hopanes. Our findings suggest that available TRAP exposure indicators have the potential for exposure misclassification in winter vs. summer and more so for LUR than for metrics of major road density. As such, limitations are evident when using traditional proxy methods for assigning traffic exposures and these may be especially important when attempting to assign exposures for children's key growth and developmental windows. If long-term chronic exposures are being estimated, our data suggest that measures of major road lengths in proximity to homes are a more-specific approach for assigning personal TRAP exposures. [ABSTRACT FROM AUTHOR]

Abstract This study investigates air pollutant concentrations across the international airshed spanning Detroit, Michigan, USA and Windsor, Ontario, Canada. The Geospatial Determinants of Health Outcomes Consortium (GeoDHOC) measured air quality concurrently in Detroit and Windsor using consistent sampling methods and locations during separate, two-week periods in September 2008 and May/June 2009. This paper presents 2009 results and compares them to previously reported 2008 campaign results to assess spatial and temporal variability. A high-density array of 100 passive and 50 active samplers was used to measure nitrogen dioxide (NO 2), 26 volatile organic compounds (VOCs), 23 polycyclic aromatic hydrocarbons (PAHs), and three size fractions of particulate matter (PM) in both campaigns. Geospatial and non-spatial tools were used to investigate changes in concentration distributions and site classification into low/medium/high tertiles between the campaigns. During the two-week 2009 campaign, general pollution patterns remained consistent with those observed during 2008. Higher concentrations in Detroit, strong correlations amongst the BTEX group, and consistency of sites falling into the low and high tertiles were observed. Similar spatial patterns for NO 2 , VOCs, BTEX, and PAHs were also found during both campaigns. Conversely, PM 1-2.5 and PM 2.5-10 distributions showed greater variability, as did toluene/benzene ratios. Observed differences are attributable to changes in point source emissions associated with changes in localized activities, possibly related to decreased economic and industrial activity in response to the downturn that began in late 2008. Repeated sampling at multiple locations is important to determine spatial and temporal variability in absolute concentrations. However, if the definition of relatively high and low concentrations regions within a well-established urban area is adequate to estimate future air pollutant exposures, a single multi-location campaign may suffice. Highlights • A 2009 air quality campaign conducted in Detroit, MI (USA) and Windsor, ON (Canada). • Results of 2009 campaign overall similar with that of the 2008 campaign. • Similar spatial patterns between the two years, with year-to-year variability. • Higher concentrations in Detroit, and strong correlations amongst the BTEX group. • Multi-year sampling essential for absolute values but not concentration patterns. [ABSTRACT FROM AUTHOR]

High density air monitoring campaigns provide spatial patterns of pollutant concentrations which are integral in exposure assessment. Such analysis can assist with the determination of links between air quality and health outcomes, however, problems due to missing data can threaten to compromise these studies. This research evaluates four methods; mean value imputation, inverse distance weighting (IDW), inter-species ratios, and regression, to address missing spatial concentration data ranging from one missing data point up to 50% missing data. BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations were measured in Windsor and Sarnia, Ontario in the fall of 2005. Concentrations and inter-species ratios were generally similar between the two cities. Benzene (B) was observed to be higher in Sarnia, whereas toluene (T) and the T/B ratios were higher in Windsor. Using these urban, industrialized cities as case studies, this research demonstrates that using inter-species ratios or regression of the data for which there is complete information, along with one measured concentration (i.e. benzene) to predict for missing concentrations (i.e. TEX) results in good agreement between predicted and measured values. In both cities, the general trend remains that best agreement is observed for the leave-one-out scenario, followed by 10% and 25% missing, and the least agreement for the 50% missing cases. In the absence of any known concentrations IDW can provide reasonable agreement between observed and estimated concentrations for the BTEX species, and was superior over mean value imputation which was not able to preserve the spatial trend. The proposed methods can be used to fill in missing data, while preserving the general characteristics and rank order of the data which are sufficient for epidemiologic studies. [ABSTRACT FROM AUTHOR]

Diesel-powered agricultural machinery (AM) is a significant contributor to air pollutant emissions, including nitrogen oxides (NOx) and particulate matter (PM). However, the fuel consumption and pollutant emissions from AM remain poorly quantified in many countries due to a lack of accurate activity data and emissions factors. In this study, the fuel consumption and air pollutant emission from AM were estimated using a survey and emission factors from the literature. A case study was conducted using data collected in Anhui, one of the agricultural provinces of China. The annual active hours of AM in Anhui ranged 130 to 175 h. The estimated diesel fuel consumption by AM was 1.45 Tg in 2013, approximately 25% of the total diesel consumption in the province. The air pollutants emitted by AM were 57 Gg of carbon monoxide, 14 Gg of hydrocarbon, 74 Gg of NOx and 5.7 Gg of PM in 2013. The NOx and PM emissions from AM were equivalent to 17% and 22% of total on-road traffic emissions in Anhui. Among nine types of AM considered, rural vehicles are the largest contributors to fuel consumption (31%) and air emissions (33–45%). [ABSTRACT FROM AUTHOR]