Decomposing PM2.5 concentrations in urban environments into meaningful factors: 1. Separating the contribution of local anthropogenic activities from background and long-range transport.

Fine particulate matter (PM2.5) is a complex mixture of aerosol particles with varying properties and sources, both local and distant. In areas lacking detailed monitoring of PM2.5 speciation, the common source-apportionment analyses are not applicable. This study demonstrates an alternative framework for estimating sources and processes that affect observed PM2.5 concentrations when information on the particle composition is unavailable. Eight years (2012-2019) of half-hourly PM2.5 observations from 10 air quality monitoring (AQM) stations, clustered according to their airmass transport sector were analyzed, using Non-negative Matrix Factorization (NMF). Factors were determined based on their variation in time, space, and between airmass sectors. Employing a supervised machine-learning model provided insights into the relationships between the extracted factors, meteorological parameters and co-measured airborne pollutants. Factor interpretations were evaluated through comparisons with measurements of PM2.5 species from a nearby Surface PARTiculate mAtter Network (SPARTAN) station. The NMF successfully separated background factors from an urban anthropogenic-activity factor, with the latter accounting for approximately 60 % of the observed PM2.5 levels in Tel Aviv (∼10±6µg/m3). Positive monotonic relationships were observed between the PM2.5 urban anthropogenic-activity factor and measurements of nitrogen oxides (NOx) and absolute humidity (AH), representing the impact of traffic emissions and hygroscopic growth, respectively. The summer background factor was found to represent long-range transport (LRT) from Europe, showing a good agreement (R2 = 0.81) with ammonium sulphate concentrations. Our results demonstrate that a spatial NMF analysis can reliably estimate contributions of different sources with distinct compositions and properties to the total observed PM2.5. Using such an analysis, future environmental health studies could assess health risks associated with exposure to distinct PM2.5 fractions. This information may assist decision makers to set environmental targets for abating PM2.5 with specific compositions and properties.

Decomposing PM2.5 concentrations in urban environments into meaningful factors 2. Extracting the contribution of traffic-related exhaust emissions.

Vehicle-emitted fine particulate matter (PM2.5) has been associated with significant health outcomes and environmental risks. This study estimates the contribution of traffic-related exhaust emissions (TREE) to observed PM2.5 using a novel factorization framework. Specifically, co-measured nitrogen oxides (NOx) concentrations served as a marker of vehicle-tailpipe emissions and were integrated into the optimization of a Non-negative Matrix Factorization (NMF) analysis to guide the factor extraction. The novel TREE-NMF approach was applied to long-term (2012-2019) PM2.5 observations from air quality monitoring (AQM) stations in two urban areas. The extracted TREE factor was evaluated against co-measured black carbon (BC) and PM2.5 species to which the TREE-NMF optimization was blind. The contribution of the TREE factor to the observed PM2.5 concentrations at an AQM station from the first location showed close agreement (R2=0.79) with monitored BC data. In the second location, a comparison of the extracted TREE factor with measurements at a nearby Surface PARTiculate mAtter Network (SPARTAN) station revealed moderate correlations with PM2.5 species commonly associated with fuel combustion, and a good linear regression fit with measured equivalent BC concentrations. The estimated concentrations of the TREE factor at the second location accounted for 7-11 % of the observed PM2.5 in the AQM stations. Moreover, analysis of specific days known to be characterized by little traffic emissions suggested that approximately 60-78 % of the traffic-related PM2.5 concentrations could be attributed to particulate traffic-exhaust emissions. The methodology applied in this study holds great potential in areas with limited monitoring of PM2.5 speciation, in particular BC, and its results could be valuable for both future environmental health research, regional radiative forcing estimates, and promulgation of tailored regulations for traffic-related air pollution abatement.

Revealing causality in the associations between meteorological variables and air pollutant concentrations.

Understanding the role of meteorology in determining air pollutant concentrations is an important goal for better comprehension of air pollution dispersion and fate. It requires estimating the strength of the causal associations between all the relevant meteorological variables and the pollutant concentrations. Unfortunately, many of the meteorological variables are not routinely observed. Furthermore, the common analysis methods cannot establish causality. Here we use the output of a numerical weather prediction model as a proxy for real meteorological data, and study the causal relationships between a large suite of its meteorological variables, including some rarely observed ones, and the corresponding nitrogen dioxide (NO2) concentrations at multiple observation locations. Time-lagged convergent cross mapping analysis is used to ascertain causality and its strength, and the Pearson and Spearman correlations are used to study the direction of the associations. The solar radiation, temperature lapse rate, boundary layer height, horizontal wind speed and wind shear were found to be causally associated with the NO2 concentrations, with mean time lags of their maximal impact at -3, -1, -2 and -3 hours, respectively. The nature of the association with the vertical wind speed was found to be uncertain and region-dependent. No causal association was found with relative humidity, temperature and precipitation.

Accounting for the aerosol type and additional satellite-borne aerosol products improves the prediction of PM2.5 concentrations.

Fine airborne particles (diameter <2.5 µm; PM2.5) are recognized as a major threat to human health due to their physicochemical properties: composition, size, shape, etc. However, normally only size-fraction-specific particle concentrations are monitored. Interestingly, although the aerosol type is reported as part of the aerosol optical depth retrieval from satellite observations, it has not been utilized, to date, as an auxiliary information/co-variate for PM2.5 prediction. We developed Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models that account for this information when predicting surface PM2.5. The models take as input only widely available data: satellite aerosol products with full cover and surface meteorological data. Distinct models were developed for AOD of specific aerosol types. Both the RF and XGBoost models performed well, showing moderate-to-high cross-validated adjusted R2 (RF: 0.753-0.909; XGBoost: 0.741-0.903), depending on the aerosol type and other covariates. The weighted performance of the specific aerosol-type models was higher than of the RF and XGBoost baseline models, where all the AOD retrievals were used together (the common practice). Our approach can provide improved risk estimates due to exposure to PM2.5, better resolved radiative forcing calculations, and tailored abatement surveillance of specific pollutants/sources.

Fusion of land use regression modeling output and wireless distributed sensor network measurements into a high spatiotemporally-resolved NO2 product.

Land use regression modeling is a common method for assessing exposure to ambient pollutants, yet it suffers from very coarse temporal resolution. Wireless distributed sensor networks (WDSN) is a promising technology that can provide extremely high spatiotemporal pollutant patterns but is known to suffer from several limitations that put into question its data reliability. This study examines the advantages of fusing data from these two methods and obtaining high spatiotemporally-resolved product that can be used for exposure assessment. We demonstrate this approach by estimating nitrogen dioxide (NO2) concentrations at a sub-urban scale, with the study area limited by the deployment of the WDSN nodes. Specifically, hourly-resolved fused-data estimates were obtained by combining a stationary traffic-based land use regression (LUR) model with observations (15 min sampling frequency) made by an array of low-cost sensor nodes, with the sensors' readings mapped over the whole study area. Data fusion was performed by merging the two independent information products using a fuzzy logic approach. The performance of the fused product was examined against reference hourly observations at four air quality monitoring (AQM) stations situated within the study area, with the AQM data not used for the development of any of the underlying information layers. The mean hourly RMSE between the fused data product and the AQM records was 9.3 ppb, smaller than the RMSE of the two base products independently (LUR: 14.87 ppb, WDSN: 10.45 ppb). The normalized Moran's I of the fused product indicates that the data-fusion product reveals more realistic spatial patterns than those of the base products. The fused NO2 concentration product shows considerable spatial variability relative to that evident by interpolation of both the WDSN records and the AQM stations data, with significant non-random patterns in 74% of the study period.

Estimation of High-Resolution PM2.5 over the Indo-Gangetic Plain by Fusion of Satellite Data, Meteorology, and Land Use Variables.

Very high spatially resolved satellite-derived ground-level concentrations of particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) have multiple potential applications, especially in air quality modeling and epidemiological and climatological research. Satellite-derived aerosol optical depth (AOD) and columnar water vapor (CWV), meteorological parameters, and land use data were used as variables within the framework of a linear mixed effect model (LME) and a random forest (RF) model to predict daily ground-level concentrations of PM2.5 at 1 km × 1 km grid resolution across the Indo-Gangetic Plain (IGP) in South Asia. The RF model exhibited superior performance and higher accuracy compared with the LME model, with better cross-validated explained variance (R2 = 0.87) and lower relative prediction error (RPE = 24.5%). The RF model revealed improved performance metrics for increasing averaging periods, from daily to weekly, monthly, seasonal, and annual means, which supported its use in estimating PM2.5 exposure metrics across the IGP at varying temporal scales (i.e., both short and long terms). The RF-based PM2.5 estimates showed high PM2.5 levels over the middle and lower IGP, with the annual mean exceeding 110 µg/m3. As for seasons, winter was the most polluted season, while monsoon was the cleanest. Spatially, the middle and lower IGP showed poorer air quality compared to the upper IGP. In winter, the middle and lower IGP experienced very poor air quality, with mean PM2.5 concentrations of >170 µg/m3.

Preexisting coronary heart disease and susceptibility to long-term effects of traffic-related air pollution: A matched cohort analysis.

BACKGROUND: Individuals with coronary heart disease are considered susceptible to traffic-related air pollution exposure. Yet, cohort-based evidence on whether preexisting coronary heart disease modifies the association of traffic-related air pollution with health outcomes is lacking. AIM: Using data of four Israeli cohorts, we compared associations of traffic-related air pollution with mortality and cancer between coronary heart disease patients and matched controls from the general population. METHODS: Subjects hospitalized with acute coronary syndrome from two patient cohorts (inception years: 1992-1993 and 2006-2014) were age- and sex-matched to coronary heart disease-free participants of two cycles of the Israeli National Health and Nutrition Surveys (inception years: 1999-2001 and 2005-2006). Ambient concentrations of nitrogen oxides at the residential place served as a proxy for traffic-related air pollution exposure across all cohorts, based on a high-resolution national land use regression model (50 m). Data on all-cause mortality (last update: 2018) and cancer incidence (last update: 2016) were retrieved from national registries. Cox-derived stratum-specific hazard ratios with 95% confidence intervals were calculated, adjusted for harmonized covariates across cohorts, including age, sex, ethnicity, neighborhood socioeconomic status, smoking, diabetes, hypertension, prior stroke and prior malignancy (the latter only in the mortality analysis). Effect-modification was examined by testing nitrogen oxides-by-coronary heart disease interaction term in the entire matched cohort. RESULTS: The cohort (mean (standard deviation) age 61.5 (14) years; 44% women) included 2393 matched pairs, among them 2040 were cancer-free at baseline. During a median (25th-75th percentiles) follow-up of 13 (10-19) and 11 (7-17) years, 1458 deaths and 536 new cancer cases were identified, respectively. In multivariable-adjusted models, a 10-parts per billion nitrogen oxides increment was positively associated with all-cause mortality among coronary heart disease patients (hazard ratio = 1.13, 95% confidence interval 1.05-1.22), but not among controls (hazard ratio = 1.00, 0.93-1.08) (pinteraction = 0.003). A similar pattern was seen for all-cancer incidence (hazard ratioCHD = 1.19 (1.03-1.37), hazard ratioCHD-Free = 0.93 (0.84-1.04) (pinteraction = 0.01)). Associations were robust to multiple sensitivity analyses. CONCLUSIONS: Coronary heart disease patients might be at increased risk for traffic-related air pollution-associated mortality and cancer, irrespective of their age and sex. Patients and clinicians should be more aware of the adverse health effects on coronary heart disease patients of chronic exposure to vehicle emissions.

Emissions vs. turbulence and atmospheric stability: A study of their relative importance in determining air pollutant concentrations.

Air pollution in the urban environment is a major concern. The ambient concentrations depend on the levels of transboundary imported pollution, the intensity of local sources and the prevailing atmospheric conditions. This work studies the relative impact of two atmospheric variables-atmospheric stability and regional scale turbulence-in determining the air pollution concentrations. We considered a setting (downtown Haifa, Israel) impacted by a large variety of sources, emitting pollutants with different chemical attributes and atmospheric life times. We found that traffic accounts for most of the locally produced pollution in the study location. However, the meteorological factors can overwhelm its impact and dictate the concentrations. The switch from stable to convective conditions and the more vigorous daytime wind are associated with a premature end of the morning peak concentrations that result from rush hour emissions of NOx, Black Carbon (BC) and ultra-fine particles. It results in daytime concentration which are lower than (winter) or equal to (summer) those at night, in spite of the much lower night-time traffic volumes. Similar, albeit weaker, impact was detected in the benzene and toluene concentrations. Sources outside the study area are responsible for most of the CO, PM1 and PM2.5 concentrations but during winter nights, characterised by strong atmospheric stability and low turbulence, their concentrations are elevated due to the local emissions. We developed a diagnostic statistical nonlinear model for the pollutant concentrations, which points to a stronger association of the atmospheric stability with the concentrations during stable conditions but turbulence dominating during convective conditions. Our findings explain the relatively low overall concentrations of locally emitted pollutants in the study area but warn of the potential for high concentrations during night-time in locations with comparable meteorological conditions.

Vertical distribution of smoke aerosols over upper Indo-Gangetic Plain.

Attenuated backscatter profiles retrieved by the space borne active lidar CALIOP on-board CALIPSO satellite were used to measure the vertical distribution of smoke aerosols and to compare it against the ECMWF planetary boundary layer height (PBLH) over the smoke dominated region of Indo-Gangetic Plain (IGP), South Asia. Initially, the relative abundance of smoke aerosols was investigated considering multiple satellite retrieved aerosol optical properties. Only the upper IGP was selectively considered for CALIPSO retrieval based on prevalence of smoke aerosols. Smoke extinction was found to contribute 2-50% of the total aerosol extinction, with strong seasonal and altitudinal attributes. During winter (DJF), smoke aerosols contribute almost 50% of total aerosol extinction only near to the surface while in post-monsoon (ON) and monsoon (JJAS), relative contribution of smoke aerosols to total extinction was highest at about 8 km height. There was strong diurnal variation in smoke extinction, evident throughout the year, with frequent abundance of smoke particles at lower height (<4 km) during daytime compared to higher height during night (>4 km). Smoke injection height also varied considerably during rice (ON: 0.71 ±â€¯0.65 km) and wheat (AM: 2.34 ±â€¯1.34 km) residue burning period having a significant positive correlation with prevailing PBLH. Partitioning smoke AOD against PBLH into the free troposphere (FT) and boundary layer (BL) yield interesting results. BL contribute 36% (16%) of smoke AOD during daytime (nighttime) and the BL-FT distinction increased particularly at night. There was evidence that despite travelling efficiently to FT, major proportion of smoke AOD (50-80%) continue to remain close to the surface (<3 km) thereby, may have greater implications on regional climate, air quality, smoke transport and AOD-particulate modelling.

Cancer and mortality in relation to traffic-related air pollution among coronary patients: Using an ensemble of exposure estimates to identify high-risk individuals.

BACKGROUND: Moderate correlations were previously observed between individual estimates of traffic-related air pollution (TRAP) produced by different exposure modeling approaches. This induces exposure misclassification for a substantial fraction of subjects. AIM: We used an ensemble of well-established modeling approaches to increase certainty of exposure classification and reevaluated the association with cancers previously linked to TRAP (lung, breast and prostate), other cancers, and all-cause mortality in a cohort of coronary patients. METHODS: Patients undergoing percutaneous coronary interventions in a major Israeli medical center from 2004 to 2014 (n = 10,627) were followed for cancer (through 2015) and mortality (through 2017) via national registries. Residential exposure to nitrogen oxides (NOx) -a proxy for TRAP- was estimated by optimized dispersion model (ODM) and land use regression (LUR) (rPearson = 0.50). Mutually exclusive groups of subjects classified as exposed by none of the methods (high-certainty low-exposed), ODM alone, LUR alone, or both methods (high-certainty high-exposed) were created. Associations were examined using Cox regression models. RESULTS: During follow-up, 741 incident cancer cases were diagnosed and 3051 deaths occurred. Using a ≥25 ppb cutoff, compared with high-certainty low exposed, the multivariable-adjusted hazard ratios (95% confidence intervals) for lung, breast and prostate cancer were 1.56 (1.13-2.15) in high-certainty exposed, 1.27 (0.86-1.86) in LUR-exposed alone, and 1.13 (0.77-1.65) in ODM-exposed alone. The association of the former category was strengthened using more extreme NOx cutoffs. A similar pattern, albeit less strong, was observed for mortality, whereas no association was shown for cancers not previously linked to TRAP. CONCLUSIONS: Use of an ensemble of TRAP exposure estimates may improve classification, resulting in a stronger association with outcomes.

Traffic-related Air Pollution and Pregnancy Loss.

BACKGROUND: Traffic-related air pollution has been linked to multiple adverse pregnancy outcomes. However, few studies have examined pregnancy loss, targeting losses identified by hospital records, a large limitation as it does not capture events not reported to the medical system. METHODS: We used a novel variation of the time-series design to determine the association, and identify the critical window of vulnerability, between week-to-week traffic-related air pollution and conceptions resulting in live births, using nitrogen dioxide (NO2) as a traffic emissions tracer. We used information from all live births recorded at Beth Israel Deaconess Medical Center in Boston, MA (2000-2013) and all live births in Tel Aviv District, Israel (2010-2013). RESULTS: In Boston (68,969 live births), the strongest association was during the 15th week of gestation; for every 10 ppb of NO2 increase during that week, we observed a lower rate of live births (rate ratio [RR] = 0.87; 95% confidence interval [CI], 0.78, 0.97), using live birth-identified conceptions to infer pregnancy losses. In the Tel Aviv District (95,053 live births), the strongest estimate was during the 16th gestational week gestation (RR = 0.82; 95% CI, 0.76, 0.90 per 10 ppb of NO2). CONCLUSIONS: Using weekly conceptions ending in live birth rather than identified pregnancy losses, we comprehensively analyzed the relationship between air pollution and all pregnancy loss throughout gestation. The observed results, with remarkable similarity in two independent locations, suggest that higher traffic-related air pollution levels are associated with pregnancy loss, with strongest estimates between the 10th and 20th gestational weeks.

Seasonal variations of polybrominated flame retardants bound to car dust under Mediterranean climate.

Polybrominated diphenyl ethers (PBDEs) are commercial flame retardants that have been commonly used in vehicle interior to reduce fire-related hazards. Due to high temperatures and intense insolation that can be attained inside cars parked in the sun, additive PBDEs are prone to leach out and attach to in-vehicle dust, as well as to photo-debrominate. This study examines seasonal variations of concentrations of three common PBDE congeners (BDE-47, BDE-99 and BDE-209) in car dust in Israel. The overall concentrations of these BDEs ranged from 1 to 29µg/g, and were higher in the summer than in the winter (average of 10.2 and 5.3µg/g, respectively). Congener-specific concentrations showed distinct seasonal pattern, representing the interplay between leaching, evaporation and photodebromination. Photolysis of the three congeners, while adsorbed on glass filters and exposed to solar radiation, revealed first order kinetics with debromination rates on the order of 10-2/min. Hence, seasonal variations of the meteorological conditions were found to affect the in-vehicle PBDE concentrations, and are therefore expected also to affect the exposure of passengers to PBDEs.

Impacts of Personal Mobility and Diurnal Concentration Variability on Exposure Misclassification to Ambient Pollutants.

Appreciating the uncertainty margins of exposure assessment to air pollution requires good understanding of its variability throughout the daily activities. This study describes a modeling framework for estimating exposure to air pollutants for a representative sample of working Israeli adults ( N ∼ 168 000) for which both the residence and workplace addresses were available. Individual daily trajectories were simulated by accounting for five generic daily activities: at home, at work, while in commute from home to work and back, and during out-of-home leisure activities. The integrated daily exposure to nitrogen dioxide (NO2) was estimated for each individual by tracking the daily trajectory through an NO2 concentration map, obtained using a dynamic and highly resolved dispersion-like model (temporal resolution, half-hourly; spatial resolution, 500 m). Accounting for the subjects' daily mobility was found to affect their exposure more significantly than accounting solely for the diurnal concentration variability, yet a synergistic effect was noted when accounting for both factors simultaneously. Exposure misclassification varies along the day, with the work microenvironment found to contribute the most to it. In particular, regardless of the high concentrations encountered during the commute, their contribution to the integrated daily exposure is small due to the relatively short time spent in this activity by most people.

Chronic exposure to traffic-related air pollution and cancer incidence among 10,000 patients undergoing percutaneous coronary interventions: A historical prospective study.

Background Exposure to traffic-related air pollution (TRAP) is considered to have a carcinogenic effect. The authors previously reported a nonsignificant association between TRAP and cancer risk in a relatively small cohort of myocardial infarction survivors. This study assessed whether TRAP exposure is associated with subsequent cancer in a large cohort of coronary patients. Methods & results Consecutive patients undergoing percutaneous coronary interventions in a major medical centre in central Israel from 2004 to 2014 were followed for cancer through 2015. Residential levels of nitrogen oxides (NOx) - a proxy for TRAP - were estimated based on a high-resolution national land use regression model. Cox proportional hazards models were constructed to study relationships with cancer. Among 12,784 candidate patients, 9816 had available exposure data and no history of cancer (mean age, 68 years; 77% men). During a median (25th-75th percentiles) follow-up of 7.0 (3.9-9.3) years, 773 incident cases of cancer (8%) were diagnosed. In a multivariable-adjusted model, a 10-ppb increase in mean NOx exposure was associated with hazard ratios (HRs) of 1.07 (95% confidence interval [CI] 1.00-1.15) for all-site cancer and 1.16 (95% CI 1.05-1.28) for cancers previously linked to TRAP (lung, breast, prostate, kidney and bladder). A stronger association was observed for breast cancer (HR = 1.43; 95% CI 1.12-1.83). Associations were slightly strengthened after limiting the cohort to patients with more precise exposure assessment. Conclusion Coronary patients exposed to TRAP are at increased risk of several types of cancer, particularly lung, prostate and breast. As these cancers are amenable to prevention strategies, identifying highly exposed patients may provide an opportunity to improve clinical care.

Node-to-node field calibration of wireless distributed air pollution sensor network.

Low-cost air quality sensors offer high-resolution spatiotemporal measurements that can be used for air resources management and exposure estimation. Yet, such sensors require frequent calibration to provide reliable data, since even after a laboratory calibration they might not report correct values when they are deployed in the field, due to interference with other pollutants, as a result of sensitivity to environmental conditions and due to sensor aging and drift. Field calibration has been suggested as a means for overcoming these limitations, with the common strategy involving periodical collocations of the sensors at an air quality monitoring station. However, the cost and complexity involved in relocating numerous sensor nodes back and forth, and the loss of data during the repeated calibration periods make this strategy inefficient. This work examines an alternative approach, a node-to-node (N2N) calibration, where only one sensor in each chain is directly calibrated against the reference measurements and the rest of the sensors are calibrated sequentially one against the other while they are deployed and collocated in pairs. The calibration can be performed multiple times as a routine procedure. This procedure minimizes the total number of sensor relocations, and enables calibration while simultaneously collecting data at the deployment sites. We studied N2N chain calibration and the propagation of the calibration error analytically, computationally and experimentally. The in-situ N2N calibration is shown to be generic and applicable for different pollutants, sensing technologies, sensor platforms, chain lengths, and sensor order within the chain. In particular, we show that chain calibration of three nodes, each calibrated for a week, propagate calibration errors that are similar to those found in direct field calibration. Hence, N2N calibration is shown to be suitable for calibration of distributed sensor networks.

Traffic-Related Air Pollution and Autism Spectrum Disorder: A Population-Based Nested Case-Control Study in Israel.

Accumulating evidence suggests that perinatal air pollutant exposures are associated with increased risk of autism spectrum disorder (ASD), but evidence for traffic pollutants outside the United States is inconclusive. We assessed the association between nitrogen dioxide, a traffic pollution tracer, and risk of ASD. We conducted a nested case-control study among the entire population of children born during 2005-2009 in the central coastal area of Israel. Cases were identified through the National Insurance Institute of Israel (n = 2,098). Controls were a 20% random sample of the remaining children (n = 54,191). Exposure was based on an optimized dispersion model. We estimated adjusted odds ratios and 95% confidence intervals using logistic regression and a distributed-lag model. In models mutually adjusted for the 2 periods, the odds ratio per 5.85-parts per billion (ppb) increment of nitrogen dioxide exposure during pregnancy (median, 16.8 ppb; range, 7.5-31.2 ppb) was 0.77 (95% confidence interval: 0.59, 1.00), and the odds ratio for exposure during the 9 months after birth was 1.40 (95% confidence interval: 1.09, 1.80). A distributed-lag model revealed reduced risk around week 13 of pregnancy and elevated risk around week 26 after birth. These findings suggest that postnatal exposure to nitrogen dioxide in Israel is associated with increased odds of ASD, and prenatal exposure with lower odds. The latter may relate to selection effects.

Wireless Distributed Environmental Sensor Networks for Air Pollution Measurement-The Promise and the Current Reality.

The evaluation of the effects of air pollution on public health and human-wellbeing requires reliable data. Standard air quality monitoring stations provide accurate measurements of airborne pollutant levels, but, due to their sparse distribution, they cannot capture accurately the spatial variability of air pollutant concentrations within cities. Dedicated in-depth field campaigns have dense spatial coverage of the measurements but are held for relatively short time periods. Hence, their representativeness is limited. Moreover, the oftentimes integrated measurements represent time-averaged records. Recent advances in communication and sensor technologies enable the deployment of dense grids of Wireless Distributed Environmental Sensor Networks for air quality monitoring, yet their capability to capture urban-scale spatiotemporal pollutant patterns has not been thoroughly examined to date. Here, we summarize our studies on the practicalities of using data streams from sensor nodes for air quality measurement and the required methods to tune the results to different stakeholders and applications. We summarize the results from eight cities across Europe, five sensor technologies-three stationary (with one tested also while moving) and two personal sensor platforms, and eight ambient pollutants. Overall, few sensors showed an exceptional and consistent performance, which can shed light on the fine spatiotemporal urban variability of pollutant concentrations. Stationary sensor nodes were more reliable than personal nodes. In general, the sensor measurements tend to suffer from the interference of various environmental factors and require frequent calibrations. This calls for the development of suitable field calibration procedures, and several such in situ field calibrations are presented.

Improving modeled air pollution concentration maps by residual interpolation.

Models that are used to map air pollutant concentrations are not free of errors. A possible approach for improving the final concentration map is to interpolate the residuals of the initial model concentration estimates. Due to the possible spatial autocorrelation of the residuals of the initial model estimates, Bayesian inference schemes were suggested for this task, since they can correctly adjust the level of fitting of the residuals to the random measurement errors. However, the complexity of Bayesian methods often discourages their use. Here, we present an alternative and simpler approach, using a leave-one-out cross-validation to determine the optimal level of fitting of the residual correction. We show that the optimal correction level is related to the extent of the spatial autocorrelation of the cross-validated residuals. Namely, when the residuals are not autocorrelated residual correction is unnecessary, and if employed may actually degrade the quality of the final concentration map. Moreover, our approach enables to optimize the residual correction based on different target performance measures, with a possibly different optimal correction depending on the performance measure used. Hence, different target performance measures can be chosen to fit best the specific application of interest. The method is demonstrated using output of three different models used for estimating NOx and NO2 concentrations over Israel. We show that our approach can be used as an exploratory step, for assessing the potential benefit of residual correction, and as a simple alternative to Bayesian schemes.

Exposure estimation errors to nitrogen oxides on a population scale due to daytime activity away from home.

Accurate estimation of exposure to air pollution is necessary for assessing the impact of air pollution on the public health. Most environmental epidemiology studies assign the home address exposure to the study subjects. Here, we quantify the exposure estimation error at the population scale due to assigning it solely at the residence place. A cohort of most schoolchildren in Israel (~950,000), age 6-18, and a representative cohort of Israeli adults (~380,000), age 24-65, were used. For each subject the home and the work or school addresses were geocoded. Together, these two microenvironments account for the locations at which people are present during most of the weekdays. For each subject, we estimated ambient nitrogen oxide concentrations at the home and work or school addresses using two air quality models: a stationary land use regression model and a dynamic dispersion-like model. On average, accounting for the subjects' work or school address as well as for the daily pollutant variation reduced the estimation error of exposure to ambient NOx/NO2 by 5-10ppb, since daytime concentrations at work/school and at home can differ significantly. These results were consistent regardless which air quality model as used and even for subjects that work or study close to their home. Yet, due to their usually short commute, assigning schoolchildren exposure solely at their residential place seems to be a reasonable estimation. In contrast, since adults commute for longer distances, assigning exposure of adults only at the residential place has a lower correlation with the daily weighted exposure, resulting in larger exposure estimation errors. We show that exposure misclassification can result from not accounting for the subjects' time-location trajectories through the spatiotemporally varying pollutant concentrations field.

Do nanoparticles provide a new opportunity for diagnosis of distal airspace disease?

There is a need for efficient techniques to assess abnormalities in the peripheral regions of the lungs, for example, for diagnosis of pulmonary emphysema. Considerable scientific efforts have been directed toward measuring lung morphology by studying recovery of inhaled micron-sized aerosol particles (0.4-1.5 µm). In contrast, it is suggested that the recovery of inhaled airborne nanoparticles may be more useful for diagnosis. The objective of this work is to provide a theoretical background for the use of nanoparticles in measuring lung morphology and to assess their applicability based on a review of the literature. Using nanoparticles for studying distal airspace dimensions is shown to have several advantages over other aerosol-based methods. 1) Nanoparticles deposit almost exclusively by diffusion, which allows a simpler breathing maneuver with minor artifacts from particle losses in the oropharyngeal and upper airways. 2) A higher breathing flow rate can be utilized, making it possible to rapidly inhale from residual volume to total lung capacity (TLC), thereby eliminating the need to determine the TLC before measurement. 3) Recent studies indicate better penetration of nanoparticles than micron-sized particles into poorly ventilated and diseased regions of the lungs; thus, a stronger signal from the abnormal parts is expected. 4) Changes in airspace dimensions have a larger impact on the recovery of nanoparticles. Compared to current diagnostic techniques with high specificity for morphometric changes of the lungs, computed tomography and magnetic resonance imaging with hyperpolarized gases, an aerosol-based method is likely to be less time consuming, considerably cheaper, simpler to use, and easier to interpret (providing a single value rather than an image that has to be analyzed). Compared to diagnosis by carbon monoxide (DL,CO), the uptake of nanoparticles in the lung is not affected by blood flow, hemoglobin concentration or alterations of the alveolar membranes, but relies only on lung morphology.