There is limited study from low-and-middle income countries on the effect of perinatal exposure to air pollution and the risk of infection in infant. We assessed the association between perinatal exposure to traffic related air pollution and the risk of infection in infant during their first six months of life. A prospective cohort study was performed in Jakarta, March 2016–September 2020 among 298 mother-infant pairs. PM2.5, soot, NOx, and NO2 concentrations were assessed using land use regression models (LUR) at individual level. Repeated interviewer-administered questionnaires were used to obtain data on infection at 1, 2, 4 and 6 months of age. The infections were categorized as upper respiratory tract (runny nose, cough, wheezing or shortness of breath), lower respiratory tract (pneumonia, bronchiolitis) or gastrointestinal tract infection. Logistic regression models adjusted for covariates were used to assess the association between perinatal exposure to air pollution and the risk of infection in the first six months of life. The average concentrations of PM2.5 and NO2 were much higher than the WHO recommended levels. Upper respiratory tract infections (URTI) were much more common in the first six months of life than diagnosed lower respiratory tract or gastro-intestinal infections (35.6

The domestic combustion of locally sourced smoky (bituminous) coal in Xuanwei and Fuyuan counties, China, is responsible for some of the highest lung cancer rates in the world. Recent research has pointed to methylated PAHs (mPAHs), particularly 5-methylchrysene (5MC), within coal combustion products as a driving factor. Here we describe measurements of mPAHs in Xuanwei and Fuyuan derived from controlled burnings (i.e., water boiling tests, WBT, n = 27) representing exposures during stove use, and an exposure assessment (EA) study (n = 116) representing 24 h weighted exposures. Using smoky coal has led to significantly higher concentrations of known and likely human carcinogens than using smokeless coal, including 5MC (3.7 ng/m3 vs. 1.0 ng/m3 for EA samples and 100.8 ng/m3 vs. 2.2 ng/m3 for WBT samples), benzo[a]pyrene (38.0 ng/m3 vs. 7.9 ng/m3 for EA samples and 455.3 ng/m3 vs. 12.0 ng/m3 for WBT samples) and 7,12-dimethylbenz[a]anthracene (1.9 ng/m3 vs. 0.2 ng/m3 for EA samples and 47.7 ng/m3 vs. 0.6 ng/m3 for WBT samples). Mixed effect models for both EA samples and WBT samples revealed clear variation in mPAHs concentrations depending on smoky coal source while stove ventilation was consistently found to reduce measured concentrations (by up to nine fold and 65 fold for EA and WBT samples respectively when using smoky coal). Fuel type had a larger influence on mPAHs concentrations than stove type. These findings indicate that users of smoky coal experience exposure to many PAHs, including known and suspected human carcinogens (especially during cooking activities), many of which are not routinely tested for. Collectively, this provides insights into the potential etiologies of lung cancer in the region and further highlights the importance of targeting clean fuel transitions and stove refinements as the final goal for reducing household air pollution and its associated health risks.

Abstract Introduction Exposure to diesel engine exhaust (DEE) is associated with increased lung cancer risk; however, underlying molecular mechanisms remain unclear. We apply an exposome approach to characterize early biological effects of occupational DEE exposure. Methods Plasma samples from 54 diesel engine factory workers and 55 non-exposed control workers were characterized using an integrated exposome platform that combines untargeted gas chromatography (GC-) and liquid chromatography (LC-) with high-resolution mass spectrometry (HRMS). Exposome profiles were evaluated by metabolome-wide association study (MWAS) for molecular features associated with DEE exposure and elemental carbon. Potential molecular mechanisms underlying DEE were further evaluated by integrating exposome profiles with plasma proteomics, urine aminopyrenes and mutagenicity, and buccal gene expression analysis. Results GC- and LC-HRMS untargeted analysis detected 68,285 metabolic features. Comparison of DEE-exposed and non-exposed workers identified 772 molecular features associated with exposure at a FDR <5%, including 102 detected using GC-HRMS and 670 detected using LC-HRMS. Molecular networking and annotation identified compounds consistent with DEE exposure, while metabolic pathway enrichment suggest alterations in oxidative stress and endothelial pathways. We conducted a secondary MWAS to link urinary mutagenicity, reflecting systemic exposure to genotoxic/carcinogenic agents, and associated with tumor development and identified 90 molecular features positively associated with urine mutagenicity at FDR<5%. Discussion Integration of exposome profiles with protein and genome-wide gene expression identified biological alterations consistent with many of the key characteristics of carcinogens. Conclusion Integrated exposome characterization of DEE exposure identified novel DEE biomarkers and biological response profiles in a high exposure setting.

Aviation has been shown to cause high particle number concentrations (PNC) in areas surrounding major airports. Particle size distribution and composition differ from motorized traffic. The objective was to study short-term effects of aviation-related UFP on respiratory health in children.In 2017–2018 a study was conducted in a school panel of 7–11 year old children (n = 161) living North and South of Schiphol Airport. Weekly supervised spirometry and exhaled nitric oxide (eNO) measurements were executed. The school panel, and an additional group of asthmatic children (n = 19), performed daily spirometry tests at home and recorded respiratory symptoms. Hourly concentrations of various size fractions of PNC and black carbon (BC) were measured at three school yards. Concentrations of aviation-related particles were estimated at the residential addresses using a dispersion model. Linear and logistic mixed models were used to investigate associations between daily air pollutant concentrations and respiratory health.PNC20, a proxy for aviation-related UFP, was virtually uncorrelated with BC and PNC50-100 (reflecting primarily motorized traffic), supporting the feasibility of separating PNC from aviation and other combustion sources. No consistent associations were found between various pollutants and supervised spirometry and eNO. Major air pollutants were significantly associated with an increase in various respiratory symptoms. Odds Ratios for previous day PNC20 per 3,598pt/cm3 were 1.13 (95%CI 1.02; 1.24) for bronchodilator use and 1.14 (95%CI 1.03; 1.26) for wheeze. Modelled aviation-related UFP at the residential addresses was also positively associated with these symptoms, corroborating the PNC20 findings. PNC20 was not associated with daily lung function, but PNC50-100 and BC were negatively associated with FEV1.PNC of different sizes indicative of aviation and other combustion sources were independently associated with an increase of respiratory symptoms and bronchodilator use in children living near a major airport. No consistent associations between aviation-related UFP with lung function was observed.

Woodsmoke from household fireplaces contributes significantly to outdoor air pollution in the Netherlands. The current understanding of the respiratory health effects of exposure to smoke from residential wood burning is limited. This study investigated the association between short-term changes in outdoor woodsmoke exposure and lung function, respiratory symptoms, and medication use in adults in the Netherlands. This study was co-created with citizen scientists and other relevant stakeholders. A panel study was conducted with repeated observations in 46 adults between February and May 2021 in four Dutch towns. Participants recorded their symptoms and medication use in daily diaries, and conducted morning and evening home spirometry measurements. Woodsmoke exposure was characterized by measuring levoglucosan (most specific marker for woodsmoke exposure), black/brown carbon, fine and ultrafine particulate matter at central monitoring sites. Individual woodsmoke perception (smell) was recorded in daily diaries. Linear and logistic regression models were used to investigate the association between respiratory health and woodsmoke exposure. Models were adjusted for time-varying confounders and accounted for repeated observations within participants. Consistent positive associations were found between levoglucosan and shortness of breath (SOB) during rest and extra respiratory medication use. Odds ratios for current day exposure to levoglucosan were 1.12 (95

Ultrafine particles (UFPs) are airborne particles with a diameter of less than 100 nm. They are emitted from various sources, such as traffic, combustion, and industrial processes, and can have adverse effects on human health. Long-term mean ambient average particle size (APS) in the UFP range varies over space within cities, with locations near UFP sources having typically smaller APS. Spatial models for lung deposited surface area (LDSA) within urban areas are limited and currently there is no model for APS in any European city. We collected particle number concentration (PNC), LDSA, and APS data over one-year monitoring campaign from May 2021 to May 2022 across 27 locations and estimated annual mean in Copenhagen, Denmark, and obtained additionally annual mean PNC data from 6 state-owned continuous monitors. We developed 94 predictor variables, and machine learning models (random forest and bagged tree) were developed for PNC, LDSA, and APS. The annual mean PNC, LDSA, and APS were, respectively, 5523 pt/cm3 , 12.0 mu m2/cm3 , and 46.1 nm. The final R2 values by random forest (RF) model were 0.93 for PNC, 0.88 for LDSA, and 0.85 for APS. The 10 -fold, repeated 10 -times cross -validation R2 values were 0.65, 0.67, and 0.60 for PNC, LDSA, and APS, respectively. The root mean square error for final RF models were 296 pt/cm3 , 0.48 mu m2/cm3 , and 1.60 nm for PNC, LDSA, and APS, respectively. Traffic -related variables, such as length of major roads within buffers 100-150 m and distance to streets with various speed limits were amongst the highly -ranked predictors for our models. Overall, our ML models achieved high R2 values and low errors, providing insights into UFP exposure in a European city where average PNC is quite low. These hyperlocal predictions can be used to study health effects of UFPs in the Danish Capital.

Abstract Background Ambient air pollution has been recognized as one of the most important environmental health threats. Exposure in early life may affect pregnancy outcomes and the health of the offspring. The main objective of our study was to assess the association between prenatal exposure to traffic related air pollutants during pregnancy on birth weight and length. Second, to evaluate the association between prenatal exposure to traffic related air pollutants and the risk of low birth weight (LBW). Methods Three hundred forty mother-infant pairs were included in this prospective cohort study performed in Jakarta, March 2016–September 2020. Exposure to outdoor PM2.5, soot, NOx, and NO2 was assessed by land use regression (LUR) models at individual level. Multiple linear regression models were built to evaluate the association between air pollutants with birth weight (BW) and birth length (BL). Logistic regression was used to assess the risk of low birth weight (LBW) associated with all air pollutants. Results The average PM2.5 concentration was almost eight times higher than the current WHO guideline and the NO2 level was three times higher. Soot and NOx were significantly associated with reduced birth length. Birth length was reduced by − 3.83 mm (95% CI -6.91; − 0.75) for every IQR (0.74 × 10− 5 per m) increase of soot, and reduced by − 2.82 mm (95% CI -5.33;-0.30) for every IQR (4.68 μg/m3) increase of NOx. Outdoor air pollutants were not significantly associated with reduced birth weight nor the risk of LBW. Conclusion Exposure to soot and NOx during pregnancy was associated with reduced birth length. Associations between exposure to all air pollutants with birth weight and the risk of LBW were less convincing.

Diesel engine exhaust (DEE) is an established lung carcinogen, but the biological mechanisms of diesel‐induced lung carcinogenesis are not well understood. MicroRNAs (miRNAs) are small noncoding RNAs that play a potentially important role in regulating gene expression related to lung cancer. We conducted a cross‐sectional molecular epidemiology study to evaluate whether serum levels of miRNAs are altered in healthy workers occupationally exposed to DEE compared to unexposed controls. We conducted a two‐stage study, first measuring 405 miRNAs in a pilot study of six DEE‐exposed workers exposed and six controls. In the second stage, 44 selected miRNAs were measured using the Fireplex circulating miRNA assay that profiles miRNAs directly from biofluids of 45 workers exposed to a range of DEE (Elemental Carbon (EC), median, range: 47.7, 6.1–79.7 μg/m3) and 46 controls. The relationship between exposure to DEE and EC with miRNA levels was analyzed using linear regression adjusted for potential confounders. Serum levels of four miRNAs were significantly lower (miR‐191‐5p, miR‐93‐5p, miR‐423‐3p, miR‐122‐5p) and one miRNA was significantly higher (miR‐92a‐3p) in DEE exposed workers compared to controls. Of these miRNAs, miR‐191‐5p (ptrend = .001, FDR = 0.04) and miR‐93‐5p (ptrend = .009, FDR = 0.18) showed evidence of an inverse exposure–response with increasing EC levels. Our findings suggest that occupational exposure to DEE may affect circulating miRNAs implicated in biological processes related to carcinogenesis, including immune function.

Diesel exhaust has long been of health concern due to established toxicity including carcinogenicity in humans. However, the precise components of diesel engine emissions that drive carcinogenesis are still unclear. Limited work has suggested that nitrated polycyclic aromatic hydrocarbons (NPAHs) such as 1-nitropyrene and 2-nitro-fluorene may be more abundant in diesel exhaust. The present study aimed to examine whether urinary amino metabolites of these NPAHs were associated with high levels of diesel engine emissions and urinary mutagenicity in a group of highly exposed workers including both smokers and nonsmokers. Spot urine samples were collected immediately following a standard work shift from each of the 54 diesel engine testers and 55 non-tester controls for the analysis of five amino metabolites of NPAHs, and cotinine (a biomarker of tobacco smoke exposure) using liquid chromatography-mass spectrometry. An overnight urine sample was collected in a subgroup of non -smoking participants for mutagenicity analysis using strain YG1041 in the Salmonella (Ames) mutagenicity assay. Personal exposure to fine particles (PM2.5) and more-diesel-specific constituents (elemental carbon and soot) was assessed for the engine testers by measuring breathing-zone concentrations repeatedly over several full work shifts. Results showed that it was 12.8 times more likely to detect 1-aminopyrene and 2.9 times more likely to detect 2-aminofluorene in the engine testers than in unexposed controls. Urinary concentrations of 1-amino-pyrene were significantly higher in engine testers (p < 0.001), and strongly correlated with soot and elemental carbon exposure as well as mutagenicity tested in strain YG1041 with metabolic activation (p < 0.001). Smoking did not affect 1-aminopyrene concentrations and 1-aminopyrene relationships with diesel exposure. In contrast, both engine emissions and smoking affected 2-aminofluorene concentrations. The results confirm that urinary 1-aminopyrene may serve as an exposure biomarker for diesel engine emissions and associated mutagenicity.

Background We previously found that occupational exposure to diesel engine exhaust (DEE) was associated with alterations to 19 biomarkers that potentially reflect the mechanisms of carcinogenesis. Whether DEE is associated with biological alterations at concentrations under existing or recommended occupational exposure limits (OELs) is unclear. Methods In a cross-sectional study of 54 factory workers exposed long-term to DEE and 55 unexposed controls, we reanalysed the 19 previously identified biomarkers. Multivariable linear regression was used to compare biomarker levels between DEE-exposed versus unexposed subjects and to assess elemental carbon (EC) exposure-response relationships, adjusted for age and smoking status. We analysed each biomarker at EC concentrations below the US Mine Safety and Health Administration (MSHA) OEL (<106 µg/m 3 ), below the European Union (EU) OEL (<50 µg/m 3 ) and below the American Conference of Governmental Industrial Hygienists (ACGIH) recommendation (<20 µg/m 3 ). Results Below the MSHA OEL, 17 biomarkers were altered between DEE-exposed workers and unexposed controls. Below the EU OEL, DEE-exposed workers had elevated lymphocytes (p=9E-03, false discovery rate (FDR)=0.04), CD4+ count (p=0.02, FDR=0.05), CD8+ count (p=5E-03, FDR=0.03) and miR-92a-3p (p=0.02, FDR=0.05), and nasal turbinate gene expression (first principal component: p=1E-06, FDR=2E-05), as well as decreased C-reactive protein (p=0.02, FDR=0.05), macrophage inflammatory protein-1β (p=0.04, FDR=0.09), miR-423-3p (p=0.04, FDR=0.09) and miR-122-5p (p=2E-03, FDR=0.02). Even at EC concentrations under the ACGIH recommendation, we found some evidence of exposure-response relationships for miR-423-3p (p trend =0.01, FDR=0.19) and gene expression (p trend =0.02, FDR=0.19). Conclusions DEE exposure under existing or recommended OELs may be associated with biomarkers reflective of cancer-related processes, including inflammatory/immune response.

OBJECTIVES:Diesel exhaust is an established human carcinogen, however the mechanisms by which it leads to cancer development are not fully understood. Mitochondrial dysfunction is an established contributor to carcinogenesis. Recent studies have improved our understanding of the role played by epigenetic modifications in the mitochondrial genome on tumorigenesis. In this study, we aim to evaluate the association between diesel engine exhaust (DEE) exposure with mitochondrial DNA (mtDNA) methylation levels in workers exposed to DEE.METHODS:The study population consisted of 53 male workers employed at a diesel engine manufacturing facility in Northern China who were routinely exposed to diesel exhaust in their occupational setting, as well as 55 unexposed male control workers from other unrelated factories in the same geographic area. Exposure to DEE, elemental carbon, organic carbon, and particulate matter (PM2.5) were assessed. mtDNA methylation for CpG sites (CpGs) from seven mitochondrial genes (D-Loop, MT-RNR1, MT-CO2, MT-CO3, MT-ATP6, MT-ATP8, MT-ND5) was measured in blood samples. Linear regression models were used to estimate the associations between DEE, elemental carbon, organic carbon and PM2.5 exposures with mtDNA methylation levels, adjusting for potential confounders.RESULTS:DEE exposure was associated with decreased MT-ATP6 (difference = -35.6%, P-value = 0.019) and MT-ATP8 methylation (difference = -30%, P-value = 0.029) compared to unexposed controls. Exposures to elemental carbon, organic carbon, and PM2.5 were also significantly and inversely associated with methylation in MT-ATP6 and MT-ATP8 genes (all P-values < 0.05).CONCLUSIONS:Our findings suggest that DEE exposure perturbs mtDNA methylation, which may be of importance for tumorigenesis.

Background and aim: In the Netherlands household fireplaces are responsible for burning about 1 billion kilograms of wood per year, emitting a significant amount of woodsmoke. Little is known about the respiratory health effects associated with the level of woodsmoke exposure present in the Netherlands. Thus, our aim was to investigate the short-term changes in lung function, respiratory symptoms and medication use associated with short-term changes in woodsmoke exposures in citizens with and without COPD/asthma. The study was co-created with citizens. Methods: We conducted a panel study with repeated observations in 46 adults (11 asthma/COPD) between February and May 2021 in four Dutch cities. Participants were asked to document symptoms and medication use in a daily diary and conduct daily home spirometry measurements in the morning and evening. Woodsmoke exposure was characterized by measuring various exposures at central sites in each study location set-up specifically for the study, and individual woodsmoke perception (smell) recorded in the daily diaries. The association between woodsmoke and health was analyzed using linear and logistic regression, adjusting for time-varying confounders and repeated observations. Results: We found significant positive associations between the specific wood smoke marker levoglucosan and shortness of breath during rest (OR 1.15 (95%CI 1.01, 1.32) per IQR increment for previous day exposure) and extra medication use (OR 1.19 (95%CI 1.07, 1.33) per IQR increment for current day exposure). We found weak associations between woodsmoke and nasal symptoms, and no consistent association with either morning or evening lung function measurements. The associations with levoglucosan remained after the inclusion of generic PM2.5 as a co-pollutant in the statistical models. Conclusion: People experienced more shortness of breath at rest, nasal symptoms and used more medication on days with higher levels of outdoor woodsmoke exposure. Keywords: Woodsmoke, Levoglucosan, Respiratory health, Panel study.

Hyperlocal air quality maps are becoming increasingly common, as they provide useful insights into the spatial variation and sources of air pollutants. In this study, we produced several high-resolution concentration maps to assess the spatial differences of three traffic-related pollutants, Nitrogen dioxide (NO2), Black Carbon (BC) and Ultrafine Particles (UFP), in Amsterdam, the Netherlands, and Copenhagen, Denmark. All maps were based on a mixed-effect model approach by using state-of-the-art mobile measurements conducted by Google Street View (GSV) cars, during October 2018 - March 2020, and Land-use Regression (LUR) models based on several land-use and traffic predictor variables. We then explored the concentration ratio between the different normalised pollutants to understand possible contributing sources to the observed hyperlocal variations. The maps developed in this work reflect, (i) expected elevated pollution concentrations along busy roads, and (ii) similar concentration patterns on specific road types, e.g., motorways, for both cities. In the ratio maps, we observed a clear pattern of elevated concentrations of UFP near the airport in both cities, compared to BC and NO2. This is the first study to produce hyperlocal maps for BC and UFP using high-quality mobile measurements. These maps are important for policymakers and health-effect studies, trying to disentangle individual effects of key air pollutants of interest (e.g., UFP).

BACKGROUND AND AIM: Fine scale exposure-assessment is needed for epidemiological studies on long-term health effects of air pollution (AP). We aimed to evaluate a) predictions of hyperlocal Google Air View-based mixed-effects land use regression (G-LUR) models for long-term AP for 2018-2020, and of ELAPSE (Effects of Low-Level AP: A Study in Europe) project LUR models for 2010 in Copenhagen, Denmark, and b) agreement of predictions between these two models. METHODS: We analyzed concentrations and Spearman correlations of ultra-fine particles (UFP), nitrogen dioxide (NO2), and black carbon (BC) by G-LUR models that predicted AP across ~30,000 streets for 2018-2020, and of NO2, BC, and fine particulate matter (PM2.5) by ELAPSE models that predicted 2010 concentrations at 100m spatial resolution (ELAPSE10). Correlation of pollutants between the two models were also assessed. Using annual mean data for 2010 and 2019 monitored at regulatory network stations, 2019 predictions were estimated for ELAPSE (ELAPSE19) NO2 and PM2.5. RESULTS: According to the G-LUR predictions, the long-term mean (SD) was 14,120 (8,849) particles/cm³ for UFP, 16.8 (8.3) μg/m³ for NO2, and 1.1 (0.4) μg/m³ for BC. According to the ELAPSE19 predictions, these were 21 (3.4) μg/m³ for NO2, and 11 (1.3) μg/m³ for PM2.5. The mean (SD) for BC was 1.6 (0.3) μg/m³ based on ELAPSE10. The correlation amongst predictions was highest between BC and NO2 (0.79 for G-LUR; 0.64 for ELAPSE). Between G-LUR and ELAPSE predictions, the highest correlation was for G-LUR NO2 and ELAPSE BC (0.64). The estimates of NO2 between the two models were moderately correlated (0.63), while for BC this was 0.51. CONCLUSIONS: Air pollution is a public health concern in Copenhagen, Denmark. There was moderate correlation between BC and NO2 predicted by both models. The moderate correlation between G-LUR and ELAPSE predictions suggests that spatial patterns have been fairly stable over 10-years.

BACKGROUND AND AIM: Recent advances in mobile monitoring offer excellent opportunities to explore the hyperlocal variation of ambient air pollution. One example is Google Street View (GSV) car equipped with high-quality instruments. Here, some challenges are translating on-road pollution levels to the building façades and the scalability of such mapping at large spatial scales, e.g. national scale. Since "traditional" deterministic modelling has been a reliable method for pollution assessment and mapping, this talk aims to explore whether deterministic modelling, via hybrid modelling, can help address the challenges mentioned above and leverage mobile monitoring for improved pollution mapping. METHODS: Three GSV cars measured hyperlocal levels of nitrogen dioxide (NO2), black carbon (BC) and Ultrafine particles (UFP) on all streets of Amsterdam (N = 46664) and Copenhagen (N = 28499) from October 2018 to March 2020. The measurements were corrected and, among others, compared with pollution estimates from national prediction models, the Danish DEHM-UBM-AirGIS, and the Dutch NSL (National Collaborative Air Quality Programme). Further, model estimates are incorporated with GSV measurements to test and apply hybrid modelling approaches using statistical (e.g. kriging) and machine learning techniques. RESULTS: Overall, Amsterdam's measured pollution levels were relatively higher (e.g. median NO2 = 24 µg/m3) than in Copenhagen (median NO2 = 13 µg/m3). In addition, GSV NO2 measurements correlated moderately (Spearman's r = 0.50) (N = 7004) with the NSL estimates in Amsterdam, whereas in Copenhagen, the Spearman's correlation (r) was in the range 0.45 – 0.67 (N = 97 and 58234). CONCLUSIONS: High-quality mobile monitoring offers a great way to study the hyperlocal variation of air pollution. Since the modelled vs measured correlation was moderate to slightly high, combining both datasets may better predict external data. The presentation will reflect on hybrid model development. KEYWORDS: Mobile measurements, Google Street View, deterministic modelling, AirGIS, hybrid model

High-resolution air quality (AQ) maps based on street-by-street measurements have become possible through large-scale mobile measurement campaigns. Such campaigns have produced data-only maps and have been used to produce empirical models [i.e., land use regression (LUR) models]. Assuming that all road segments are measured, we developed a mixed model framework that predicts concentrations by an LUR model, while allowing road segments to deviate from the LUR prediction based on between-segment variation as a random effect. We used Google Street View cars, equipped with high-quality AQ instruments, and measured the concentration of NO2 on every street in Amsterdam (n = 46.664) and Copenhagen (n = 28.499) on average seven times over the course of 9 and 16 months, respectively. We compared the data-only mapping, LUR, and mixed model estimates with measurements from passive samplers (n = 82) and predictions from dispersion models in the same time window as mobile monitoring. In Amsterdam, mixed model estimates correlated rs (Spearman correlation) = 0.85 with external measurements, whereas the data-only approach and LUR model estimates correlated rs = 0.74 and 0.75, respectively. Mixed model estimates also correlated higher rs = 0.65 with the deterministic model predictions compared to the data-only (rs = 0.50) and LUR model (rs = 0.61). In Copenhagen, mixed model estimates correlated rs = 0.51 with external model predictions compared to rs = 0.45 and rs = 0.50 for data-only and LUR model, respectively. Correlation increased for 97 locations (rs = 0.65) with more detailed traffic information. This means that the mixed model approach is able to combine the strength of data-only mapping (to show hyperlocal variation) and LUR models by shrinking uncertain concentrations toward the model output.

We investigated whether exposure to carcinogenic diesel engine exhaust (DEE) was associated with altered adduct levels in human serum albumin (HSA) residues. Nano-liquid chromatography-high resolution mass spectrometry (nLC-HRMS) was used to measure adducts of Cys34 and Lys525 residues in plasma samples from 54 diesel engine factory workers and 55 unexposed controls. An untargeted adductomics and bioinformatics pipeline was used to find signatures of Cys34/Lys525 adductome modifications. To identify adducts that were altered between DEE-exposed and unexposed participants, we used an ensemble feature selection approach that ranks and combines findings from linear regression and penalized logistic regression, then aggregates the important findings with those determined by random forest. We detected 40 Cys34 and 9 Lys525 adducts. Among these findings, we found evidence that 6 Cys34 adducts were altered between DEE-exposed and unexposed participants (i.e., 841.75, 851.76, 856.10, 860.77, 870.43, and 913.45). These adducts were biologically related to antioxidant activity.

BACKGROUND AND AIM: Google Street View (GSV) cars provide air pollution (AP) data across thousands of streets in multiple cities. Various methods exist for linkage of such vector data with populations. While rasterization or near-analysis are possible methods, multiple streets often surround residences; thus, a composite value can be assigned for geo-locations. We aimed to identify best geospatial method for exposure assignment from such data. METHODS: Long-term mean AP [ultra-fine particles (UFP), nitrogen dioxide (NO2), and black carbon (BC)] predictions across 30,312 streets (length = 15-60 m) were obtained from GSV-based mixed-effects LUR models developed for Copenhagen, Denmark. A near-analysis was used where Euclidean distances between each residence (out of ~77,000) and surrounding streets were calculated, nearest street was identified, and its AP values were assigned. Predictions were also assigned to mid-street centroid; using a systematic algorithm data were split to train (24,061; ~80%) and test sets (3,031; ~10%). Spatial averaging (SA), inverse distance weighting (IDW), ordinary kriging (OK), and natural neighbor (NN) models with multiple configurations for weighting and cell-size were developed. The coefficient of determination (R2) and RMSE were calculated on the test sets. RESULTS: The mean (SD) of UFP, NO2, and BC were, respectively, 14,120 (8,849) particles/cm³, 16.8 (8.3) μg/m³, and 1.1 (0.4) μg/m³. Overall, 9 SA, 27 IDW, 45 OK, and 3 NN models were developed. NN with a cell-size of 15m was the best performing model. The R2 and RMSE for NN on the test sets were, respectively, 0.92 and 2543 pt/cm3 for UFP, 0.87 and 3.1 µg/m3 for NO2, and 0.88 and 0.15 µg/m3 for BC. The Spearman correlation between residential predictions from NN and near-analysis assignment method was 0.97 for UFP, 0.95 for NO2, and 0.93 for BC. CONCLUSIONS: Although high correlation was observed for NN and near-analysis, the latter overestimated the concentrations.

Urinary mutagenicity reflects systemic exposure to complex mixtures of genotoxic/carcinogenic agents and is linked to tumor development. Coal combustion emissions (CCE) and diesel engine exhaust (DEE) are associated with cancers of the lung and other sites, but their influence on urinary mutagenicity is unclear. We investigated associations between exposure to CCE or DEE and urinary mutagenicity. In two separate cross-sectional studies of nonsmokers, organic extracts of urine were evaluated for mutagenicity levels using strain YG1041 in the Salmonella (Ames) mutagenicity assay. First, we compared levels among 10 female bituminous (smoky) coal users from Laibin, Xuanwei, China, and 10 female anthracite (smokeless) coal users. We estimated exposure–response relationships using indoor air concentrations of two carcinogens in CCE relevant to lung cancer, 5-methylchrysene (5MC), and benzo[ a ]pyrene (B[ a ]P). Second, we compared levels among 20 highly exposed male diesel factory workers and 15 unexposed male controls; we evaluated exposure-response relationships using elemental carbon (EC) as a DEE-surrogate. Age-adjusted linear regression was used to estimate associations. Laibin smoky coal users had significantly higher average urinary mutagenicity levels compared to smokeless coal users (28.4 ± 14.0 SD vs. 0.9 ± 2.8 SD rev/ml-eq, p  = 2 × 10 −5 ) and a significant exposure-response relationship with 5MC ( p  = 7 × 10 −4 ). DEE-exposed workers had significantly higher urinary mutagenicity levels compared to unexposed controls (13.0 ± 10.1 SD vs. 5.6 ± 4.4 SD rev/ml-eq, p  = .02) and a significant exposure-response relationship with EC ( p -trend = 2 × 10 −3 ). Exposure to CCE and DEE is associated with urinary mutagenicity, suggesting systemic exposure to mutagens, potentially contributing to cancer risk and development at various sites.

The domestic combustion of smoky (bituminous) coal in the Chinese counties of Xuanwei and Fuyuan, are responsible for some of the highest rates of lung cancer in the world. Cancer rates vary between coal producing regions (deposits) in the area, with coals from Laibin exhibiting particularly high risks and smokeless (anthracite) coal exhibiting lower risks. However, little information is available on the specific burning characteristics of coals from throughout the area. We conducted an extensive controlled burning experiment using coal from multiple deposits in either a traditional firepit or ventilated stove, accompanied by a detailed examination of time-weighted and real-time size-aggregated particle concentrations. Smoky coal caused higher particle concentrations of all sizes than smokeless coal, with variations observed by geological source. Virtually all particle emissions were in the PM2.5 fraction (98% - mass based), and 75% and 46% were in the PM1 and PM0.3 fraction respectively. Real-time concentrations of PM1 and PM0.1 peaked after coal was added and declined afterwards. Ventilation reduced particle concentrations by up to 15-fold and increased the coal burning rate by 1.9-fold. These findings may provide valuable insight for reducing exposure and adverse health effects associated with domestic coal combustion.