Reassessing the availability of crop residue as a bioenergy resource in India: A field-survey based study.

Second-generation bioenergy, a carbon neutral or negative renewable resource, is crucial to achieving India's net-zero emission targets. Crop residues are being targeted as a bioenergy resource as they are otherwise burned on-field, leading to significant pollutant emissions. But estimating their bioenergy potential is problematic because of broad assumptions about their surplus fractions. Here, we use comprehensive surveys and multivariate regression models to estimate the bioenergy potential of surplus crop residues in India. These are with high sub-national and crop disaggregation that can facilitate the development of efficient supply chain mechanisms for its widespread usage. The estimated potential for 2019 of 1313 PJ can increase the present bioenergy installed capacity by 82% but is likely insufficient alone to meet India's bioenergy targets. The shortage of crop residue for bioenergy, combined with the sustainability concerns raised by previous studies, imply a need to reassess the strategy for the use of this resource.

On-road vehicular emission characterization from the road-tunnel measurements in India: Morphology, emission factors, and sources.

In India, there is very limited data on vehicular emission characterization in real-world driving conditions and the contribution of non-exhaust vehicular emissions to ambient particulate matter (PM) is still unanswered. Furthermore, there are no real-world emission factors (EFs) for the PM constituents. Thus, this study aims to characterize the trace elements and metals, and black carbon (BC) in PM2.5 and PM10 from the light-duty vehicles (LDVs) and mixed vehicular fleet with significant contribution of heavy-duty vehicles (HDVs) through road-tunnel measurements. Real-world EFs were estimated for the measured PM chemical constituents. Further, source apportionment was carried out to find the plausible sources and their contribution to total PM2.5 and PM10 road traffic emissions. Air pollutant and traffic measurements were conducted at two roadway tunnels: Eastern Freeway tunnel (FT; only LDVs) and Kamshet-I tunnel (KT; 80% LDVs & 20% HDVs) in Mumbai, India covering both peak and off-peak traffic hours. Major elements (Al, Ca, Fe, K, Mg, and Na) constitute 90─93% of total measured elemental concentrations in both PM2.5 and PM10 road traffic emissions. Overall, the elemental concentrations were higher for the HDV-dominant fleet than the LDV-fleet for both PM2.5 and PM10. Similarly, BC was higher for the HDV-dominant fleet which is corroborated by the morphological analysis. The measured BC, trace elements and metals EFs in this study were higher than those reported than previous road tunnel studies with similar vehicle composition indicating the presence of high-emitting vehicles in this study. The dominant proportion of PM2.5 road traffic emissions was from the tailpipe (52%) followed by brake wear (30%) and vehicular driven resuspended road dust (18%). Whilst, resuspended road dust (63%) was identified as the major source of PM10 traffic emissions followed by vehicular exhaust (28%) and brake wear (9%). With the potential increase in the share of electric and hybrid vehicles in the vehicular fleet, the relative contribution of non-exhaust emissions to the airborne PM will be more significant. Hence, there is an imminent need to regulate non-exhaust vehicular emissions.

Chemical characteristics and oxidative potential of indoor and outdoor PM2.5 in densely populated urban slums.

A significant proportion of population in metropolitan cities in India live in slums which are highly dense and crowded informal housing settlements with poor environmental conditions including high exposure to air pollution. Recent studies report that toxicity is induced by oxidative processes, mediated by the water-soluble PM chemical components leading to reactive oxygen species production thereby causing inflammatory disorders. Hence, for the first time, this study assessed the chemical characteristics and oxidative potential (OP) of indoor and outdoor PM2.5 in two slums in Mumbai, India. Daily gravimetric PM2.5 was measured in ∼40 homes each in a low- and a high-traffic slum and analysed for 18 water-soluble elements and organic carbon (WSOC). Subsequently, OP was assessed through the Dithiothreitol (DTT) assay. Average WSOC was similar in indoor and outdoor environments while the water-soluble concentrations of total elements ranged 4.5-6.5 µg/m3 indoors and 6.4-19.2 µg/m3 outdoors, with S, Ca, K, Na and Zn being the most abundant elements. Spatial distributions of indoor concentrations were influenced by outdoor sources such as local traffic emissions for Cd, Fe, Al and Zn. The influence of outdoor-origin particles was enhanced in homes reporting high air exchange rates. OP was higher outdoors than indoors in both low-traffic slum (0.04-0.51 nmol min-1m-3 outdoors and 0.02-0.38 nmol min-1m-3 indoors) and high-traffic slum (0.03-1.06 nmol min-1m-3 outdoors and 0.04-0.77 nmol min-1m-3 indoors). Outdoor and indoor OP was also more influenced by outdoor road dust showing significant correlation with tracer elements Cu and Al (r ≥ 0.45; p < 0.05). Similar to OP, the non-carcinogenic health risk associated with indoor PM2.5 were also higher in high-traffic slum (Hazard Index, HI = 1.60) than in low-traffic slum (HI = 0.43). Overall, this study shows that the indoor PM2.5 and its chemical constituents in Mumbai slums are primarily of outdoor origin with higher toxicity and non-carcinogenic health risk in high-traffic slums.

Development of land use regression models for nitrogen dioxide, ultrafine particles, lung deposited surface area, and four other markers of particulate matter pollution in the Swiss SAPALDIA regions.

BACKGROUND: Land Use Regression (LUR) is a popular method to explain and predict spatial contrasts in air pollution concentrations, but LUR models for ultrafine particles, such as particle number concentration (PNC) are especially scarce. Moreover, no models have been previously presented for the lung deposited surface area (LDSA) of ultrafine particles. The additional value of ultrafine particle metrics has not been well investigated due to lack of exposure measurements and models. METHODS: Air pollution measurements were performed in 2011 and 2012 in the eight areas of the Swiss SAPALDIA study at up to 40 sites per area for NO2 and at 20 sites in four areas for markers of particulate air pollution. We developed multi-area LUR models for biannual average concentrations of PM2.5, PM2.5 absorbance, PM10, PMcoarse, PNC and LDSA, as well as alpine, non-alpine and study area specific models for NO2, using predictor variables which were available at a national level. Models were validated using leave-one-out cross-validation, as well as independent external validation with routine monitoring data. RESULTS: Model explained variance (R(2)) was moderate for the various PM mass fractions PM2.5 (0.57), PM10 (0.63) and PMcoarse (0.45), and was high for PM2.5 absorbance (0.81), PNC (0.87) and LDSA (0.91). Study-area specific LUR models for NO2 (R(2) range 0.52-0.89) outperformed combined-area alpine (R (2) = 0.53) and non-alpine (R (2) = 0.65) models in terms of both cross-validation and independent external validation, and were better able to account for between-area variability. Predictor variables related to traffic and national dispersion model estimates were important predictors. CONCLUSIONS: LUR models for all pollutants captured spatial variability of long-term average concentrations, performed adequately in validation, and could be successfully applied to the SAPALDIA cohort. Dispersion model predictions or area indicators served well to capture the between area variance. For NO2, applying study-area specific models was preferable over applying combined-area alpine/non-alpine models. Correlations between pollutants were higher in the model predictions than in the measurements, so it will remain challenging to disentangle their health effects.

Adult lung function and long-term air pollution exposure. ESCAPE: a multicentre cohort study and meta-analysis.

The chronic impact of ambient air pollutants on lung function in adults is not fully understood. The objective of this study was to investigate the association of long-term exposure to ambient air pollution with lung function in adult participants from five cohorts in the European Study of Cohorts for Air Pollution Effects (ESCAPE). Residential exposure to nitrogen oxides (NO2, NOx) and particulate matter (PM) was modelled and traffic indicators were assessed in a standardised manner. The spirometric parameters forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) from 7613 subjects were considered as outcomes. Cohort-specific results were combined using meta-analysis. We did not observe an association of air pollution with longitudinal change in lung function, but we observed that a 10 µg·m(-3) increase in NO2 exposure was associated with lower levels of FEV1 (-14.0 mL, 95% CI -25.8 to -2.1) and FVC (-14.9 mL, 95% CI -28.7 to -1.1). An increase of 10 µg·m(-3) in PM10, but not other PM metrics (PM2.5, coarse fraction of PM, PM absorbance), was associated with a lower level of FEV1 (-44.6 mL, 95% CI -85.4 to -3.8) and FVC (-59.0 mL, 95% CI -112.3 to -5.6). The associations were particularly strong in obese persons. This study adds to the evidence for an adverse association of ambient air pollution with lung function in adults at very low levels in Europe.

Ambient ultrafine particle levels at residential and reference sites in urban and rural Switzerland.

Although there is evidence that ultrafine particles (UFP) do affect human health there are currently no legal ambient standards. The main reasons are the absence of spatially resolved exposure data to investigate long-term health effects and the challenge of defining representative reference sites for monitoring given the high dependence of UFP on proximity to sources. The objectives of this study were to evaluate the spatial distribution of UFP in four areas of the Swiss Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA) and to investigate the representativeness of routine air monitoring stations for residential sites in these areas. Repeated UFP measurements during three seasons have been conducted at a total of 80 residential sites and four area specific reference sites over a median duration of 7 days. Arithmetic mean residential PNC scattered around the median of 10,800 particles/cm(3) (interquartile range [IQR] = 7800 particles/cm(3)). Spatial within area contrasts (90th/10th percentile ratios) were around two; increased contrasts were observed during weekday rush-hours. Temporal UFP patterns were comparable at reference and residential sites in all areas. Our data show that central monitoring sites can represent residential conditions when locations are well chosen with respect to the local sources--namely traffic. For epidemiological research, locally resolved spatial models are needed to estimate individuals' long-term exposures to UFP of outdoor origin at home, during commute and at work.

Land use regression models for crustal and traffic-related PM2.5 constituents in four areas of the SAPALDIA study.

Many studies have documented adverse health effects of long-term exposure to fine particulate matter (PM2.5), but there is still limited knowledge regarding the causal relationship between specific sources of PM2.5 and such health effects. The spatial variability of PM2.5 constituents and sources, as a exposure assessment strategy for investigating source contributions to health effects, has been little explored so far. Between 2011 and 2012, three measurement campaigns of PM and nitrogen dioxide (NO2) were performed in 80 sites across four areas of the Swiss Study on Air Pollution and Lung and heart Diseases in Adults (SAPALDIA). Reflectance analysis and energy dispersive X-ray fluorescence (XRF) were performed on PM2.5 filter samples to estimate light absorbance and trace element concentrations, respectively. Three air pollution source factors were identified using principal-component factor analysis: vehicular, crustal, and long-range transport. Land use regression (LUR) models were developed for temporally-adjusted scores of each factor, combining the four study areas. Model performance was assessed using two cross-validation methods. Model explained variance was high for the vehicular factor (R(2)=0.76), moderate for the crustal factor (R(2)=0.46), and low for the long-range transport factor (R(2)=0.19). The cross-validation methods suggested that models for the vehicular and crustal factors moderately accounted for both the between and within-area variability, and therefore can be applied to the four study areas to estimate long-term exposures within the SAPALDIA study population. The combination of source apportionment techniques and LUR modelling may help in identifying air pollution sources and disentangling their contribution to observed health effects in epidemiologic studies.

Association of ambient air pollution with the prevalence and incidence of COPD.

The role of air pollution in chronic obstructive pulmonary disease (COPD) remains uncertain. The aim was to assess the impact of chronic exposure to air pollution on COPD in four cohorts using the standardised ESCAPE exposure estimates. Annual average particulate matter (PM), nitrogen oxides (NOx) and road traffic exposure were assigned to home addresses using land-use regression models. COPD was defined by NHANES reference equation (forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) less than the lower limit of normal) and the Global Initiative for Chronic Obstructive Lung Disease criterion (FEV1/FVC <0.70) and categorised by severity in non-asthmatics. We included 6550 subjects with assigned NOx and 3692 with PM measures. COPD was not associated with NO2 or PM10 in any individual cohort. In meta-analyses only NO2, NOx, PM10 and the traffic indicators were positively, although not significantly, associated with COPD. The only statistically significant associations were seen in females (COPD prevalence using GOLD: OR 1.57, 95% CI 1.11-2.23; and incidence: OR 1.79, 95% CI 1.21-2.68). None of the principal results were statistically significant, the weak positive associations of exposure with COPD and the significant subgroup findings need to be evaluated in further well standardised cohorts followed up for longer time, and with time-matched exposure assignments.

Characterization and apportionment of carbonaceous aerosol emission factors from light-duty and heavy-duty vehicle fleets in Maharashtra, India.

This study aims to investigate the characteristics of carbonaceous aerosols and estimate emission factor (EF) based on roadway tunnel measurements, from two distinct vehicular fleets: an all light-duty vehicle (LDV) fleet, and a mixed fleet of 80% LDV and 20% heavy-duty vehicle (HDV). Carbonaceous content (organic carbon: OC and elemental carbon: EC) in total fine particles (PM2.5) accounted for 41% ± 6.8% in LDV fleet and 48% ± 7.2% in mixed fleet. While higher volatile OC dominated in the LDV fleet emissions, in mixed fleet, lower volatile OC and EC emissions dominated due to the presence of higher HDV and super-emitter (SE) fractions which led to significantly higher optically active absorbing aerosols. Reconstructed HDV fleet EF was higher than LDV fleet by 36 times (PM2.5), 19 times (OC) and 51 times (EC). Our findings emphasize the significance of implementing vehicle inspection and maintenance programs, coupled with decarbonization of HDVs to mitigate on-road vehicular emissions in India.

Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas: the ESCAPE project.

Land use regression models (LUR) frequently use leave-one-out-cross-validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO2) and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO2. LUR models have been developed for NO2, PM2.5 absorbance, and copper (Cu) in PM10 based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and "hold-out evaluation (HEV)" using the correlation of predicted NO2 or PM concentrations with measured NO2 concentrations at the 20 additional NO2 sites in each area. For NO2, PM2.5 absorbance and PM10 Cu, the median LOOCV R(2)s were 0.83, 0.81, and 0.76 whereas the median HEV R(2) were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R(2) and HEV R(2) for PM2.5 absorbance and PM10 Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV R(2)s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites.

Chemical and oxidative properties of fine particulate matter from near-road traffic sources.

The toxicity associated with the fine particulate matter (PM2.5) has not been well studied, particularly in relation to the emissions from on-road vehicles and other sources in low- and middle-income countries such as India. Thus, a study was conducted to examine the oxidative potential (OP) of PM2.5 at a roadside (RS) site with heavy vehicular traffic and an urban background (BG) site in Mumbai using the dithiothreitol (DTT) assay. Simultaneous gravimetric PM2.5 was measured at both sites and characterized for carbonaceous constituents and water-soluble trace elements and metals. Results depicted higher PM2.5, elemental carbon (EC), and organic carbon (OC) concentrations on the RS than BG (by a factor of 1.7, 4.6, and 1.2, respectively), while BG had higher water-soluble organic carbon (WSOC) levels (by a factor of 1.4) and a higher WSOC to OC ratio (86%), likely due to the dominance of secondary aerosol formation. In contrast, the measured OPDTTv at RS (8.9 ± 5.5 nmol/min/m3) and BG (8.1 ± 6.4 nmol/min/m3) sites were similar. However, OPDTTv at BG was higher during the afternoon, suggesting the influence of photochemical transformation on measured OPDTTv at BG. At RS, OC and redox-active metals (Cu, Zn, Mn, and Fe) were significantly associated with measured OP (p < 0.05), while at BG, WSOC was most strongly associated (p < 0.05). The coefficient of divergence (COD) for PM2.5, its chemical species, and OPDTTv was >0.2, indicating spatial heterogeneity between the sites, and differences in emission sources and toxicity. The estimated hazard index (HI) was not associated with OPDTTv, indicating that current PM2.5 mass regulations may not adequately capture the health effects of PM2.5. The study highlights the need for further studies examining PM2.5 toxicity and developing toxicity-based air quality regulations.

Inequalities in occupational exposures among people using popular commute modes.

Several recent studies have looked into the differences in air qualities inside popular commute modes. The impact of daily commuting patterns and work-related trips on inhalation doses, however, are not investigated. The purpose of this study is to quantify the variation in air pollutants within popular commute modes in Mumbai, India, and to estimate the variation in exposure as a result of occupational or work-related trips across different sub-groups. Real-time pollutants, both gaseous and particulate matters (PM), were measured on a pre-defined route during rush and non-rush hours on buses, cars, auto-rickshaws, sub-urban trains, and motorbikes through several trips (N = 98). Household surveys were conducted to estimate the exposures of different occupational subgroups (cab-driver, auto-rickshaw drivers, delivery persons) and people commuting to their offices daily. Participants (N = 800) from various socioeconomic backgrounds in the city were asked about their job categories, work-activity patterns, and work-related commute trips. Mass concentrations of particles in different size ranges (PM1, PM2.5, and PM10) were substantially higher (p < 0.05) inside auto-rickshaws (44.6 µg/m3, 84.7 µg/m3, and 138.3 µg/m3) compared to other modes. Inside cars, gaseous pollutants such as carbon monoxide (CO) and total volatile organic compounds (TVOC) were significantly higher (p < 0.05). Although both gaseous and particulate concentrations were lower (p < 0.05) inside buses, bus-commuters were found to be highly exposed to the pollutants due to the extended trip time (∼1.2 times longer than other modes) and driving conditions. Office commuters inhale a large fraction of their daily doses (25-30%) during their work-related travel. Occupational sub-groups, on the other hand, inhale ∼90% of the pollutants during their work. In a day, an auto-rickshaw driver inhales 10-15% more (p < 0.05) pollutants than cab driver or delivery personnel. Therefore, this study highlights the inequalities in occupational exposure as a combined effect of in-cabin air qualities and commute patterns due to occupational obligations.

1H NMR structural signatures of source and atmospheric organic aerosols in India.

Organic aerosols (OA) play significant roles in several atmospheric processes and adversely impact human health. This study examines the key structural units present in water- and methanol-soluble organic carbon (WSOC, MSOC) fraction of OA from emission sources (traffic and biomass cooking) and an urban background location in India. Proton nuclear magnetic resonance (1H NMR) spectroscopy was employed to assess the distribution of non-exchangeable proton structural groups of the OAs. Organic carbon, elemental carbon, black carbon, and water-soluble organic carbon (WSOC) analyses were also conducted. The 1H NMR analysis corroborated that the WSOC and MSOC fractions hold similar 1H structural groups; however, they differ in their relative distribution and absolute concentrations across the ambient locations and source emissions. The relative contribution of the proton structural groups to OA was in the order C-H > H-C-C=> H-C-O > Ar-H. The aliphatic concentration was lower in the morning tunnel entry aerosols when compared to other tunnel aerosols, whereas the unsaturated structures (H-C-C= ) were present in all the tunnel aerosols within a range of 47.2-62.3 µmol/m3. The aromatic groups were the maximum in the firewood aerosols, about 1.4 and 3.7 times higher than the crop residue and the mixed fuel aerosols, respectively. The total functional groups, i.e., the sum of all the observed groups, significantly correlated with C-H (r = 0.96) and WSOC (r = 0.7), suggesting the higher contribution of aliphatic groups in the WSOC fraction. WSOC examined in this study fits well in the established 1H NMR source identification fingerprints of urban aerosols. However, biomass cooking aerosols do not fit the established biomass burning organic aerosols (BBOAs) boundaries, exhibiting a smaller relative contribution of carbon-oxygen double bonds and a less oxidised character than open-field burning. Our results provide essential insights into the nature of urban atmospheric, near-traffic and biomass cooking OAs in India.

Local determinants of road traffic noise levels versus determinants of air pollution levels in a Mediterranean city.

BACKGROUND: Both traffic-related noise and air pollution have been associated with cardiovascular disease (CVD). Spatial correlations between these environmental stressors may entail mutual confounding in epidemiological studies investigating their long-term effects. Few studies have investigated their correlation - none in Spain - and results differ among cities. OBJECTIVES: We assessed the contribution of urban land-use and traffic variables to the noise-air pollution correlation in Girona town, where an investigation of the chronic effects of air pollution and noise on CVD takes place (REGICOR-AIR). METHODOLOGY: Outdoor annual mean concentrations of nitrogen dioxide (NO(2)) derived from monthly passive sampler measurements were obtained at 83 residential locations. Long-term average traffic-related noise levels from a validated model were assigned to each residence. Linear regression models were fitted both for NO(2) and noise. RESULTS: The correlation between NO(2) and noise (L(24h)) was 0.62. However, the correlation differed across the urban space, with lower correlations at sites with higher traffic density and in the modern downtown. Traffic density, distance from the location to the sidewalk and building density nearby explained 35.6% and 73.2% of the variability of NO(2) and noise levels, respectively. The correlation between the residuals of the two models suggested the presence of other unmeasured common variables. CONCLUSIONS: The substantial correlation between traffic-related noise and NO(2), endorsed by common determinants, and the dependence of this correlation on complex local characteristics call for careful evaluations of both factors to ultimately assess their cardiovascular effects.

Spatial and seasonal variation of outdoor BC and PM2.5 in densely populated urban slums.

A large proportion of residents in urban centers of low- and middle-income countries live in low-socioeconomic neighborhoods called "slums" characterized by low-cost housings of high population density, poor ventilation, and likely poor air quality. This study provides the first quantitative assessment of spatial and seasonal variation of outdoor BC and PM2.5 concentrations in several densely populated slums of Mumbai, India. Mobile outdoor real-time BC and PM2.5 monitoring was conducted along pre-designed monitoring routes in seven slums in Mumbai during the summer (May-June 2015 and May 2016) and repeated in four of them during the winter (February 2016). The measurements were repeated on the routes during different hours and days to account for the temporal variability of air pollution (nsummer = 80 trips; nwinter = 48 trips). PM2.5 exhibited homogenous distribution inside each slum (coefficient of divergence (COD) = 0.11-0.23), while BC varied significantly showing increasing concentrations with proximity to major roads (COD = 0.26-0.64). BC/PM2.5 ratio, an indicator of impact of traffic emissions, was higher along major roads of all slums (14-43%) and minor roads and alleys of high-traffic slums (10-17%) while lowest along alleys and minor roads of low-traffic slums (7-11%). Comparison of pollutant concentrations among major roads revealed the dominant effect of emissions from heavy-duty vehicles and traffic congestion. Significantly high concentrations were observed during winter season compared with summer for both PM2.5 (125 ± 46 µg m-3 in winter and 41 ± 25 µg m-3 in summer) and BC (12 ± 6 µg m-3 in winter and 7 ± 6 µg m-3 in summer). The results of this study indicate that slum residents in Mumbai and similar slums around the world are at a higher risk of traffic-related air pollution, with risk being more severe in winters due to poorer dispersion conditions. Our findings suggest that targeted mitigation strategies to reduce vehicular emissions, especially in high-traffic slums, would yield required benefits.Graphical abstract.

Quantification of Self Pollution from Two Diesel School Buses using Three Independent Methods.

We monitored two Seattle school buses to quantify the buses' self pollution using the dual tracers (DT), lead vehicle (LV), and chemical mass balance (CMB) methods. Each bus drove along a residential route simulating stops, with windows closed or open. Particulate matter (PM) and its constituents were monitored in the bus and from a LV. We collected source samples from the tailpipe and crankcase emissions using an on-board dilution tunnel. Concentrations of PM(1), ultrafine particle counts, elemental and organic carbon (EC/OC) were higher on the bus than the LV. The DT method estimated that the tailpipe and the crankcase emissions contributed 1.1 and 6.8 mug/m(3) of PM(2.5) inside the bus, respectively, with significantly higher crankcase self pollution (SP) when windows were closed. Approximately two-thirds of in-cabin PM(2.5) originated from background sources. Using the LV approach, SP estimates from the EC and the active personal DataRAM (pDR) measurements correlated well with the DT estimates for tailpipe and crankcase emissions, respectively, although both measurements need further calibration for accurate quantification. CMB results overestimated SP from the DT method but confirmed crankcase emissions as the major SP source. We confirmed buses' SP using three independent methods and quantified crankcase emissions as the dominant contributor.

Clinical, Epidemiological and Experimental Approaches to Assess Adverse Health Outcomes of Indoor Biomass Smoke Exposure: Conclusions from An Indo-Swedish Workshop in Mysuru, January 2020.

This report summarizes the outcome of a workshop held in Mysuru, India in January 2020 addressing the adverse health effects of exposure to biomass smoke (BMS). The aim of the workshop was to identify uncertainties and gaps in knowledge and possible methods to address them in the Mysuru study on Determinants of Health in Rural Adults (MUDHRA) cohort. Specific aims were to discuss the possibility to improve and introduce new screening methods for exposure and effect, logistic limitations and other potential obstacles, and plausible strategies to overcome these in future studies. Field visits were included in the workshop prior to discussing these issues. The workshop concluded that multi-disciplinary approaches to perform: (a) indoor and personalized exposure assessment; (b) clinical and epidemiological field studies among children, adolescents, and adults; (c) controlled exposure experiments using physiologically relevant in vitro and in vivo models to understand molecular patho-mechanisms are warranted to dissect BMS-induced adverse health effects. It was perceived that assessment of dietary exposure (like phytochemical index) may serve as an important indicator for understanding potential protective mechanisms. Well trained field teams and close collaboration with the participating hospital were identified as the key requirements to successfully carry out the study objectives.

Origin and properties of soluble brown carbon in freshly emitted and aged ambient aerosols over an urban site in India.

This work investigates the absorption properties of soluble brown carbon (BrC), extracted in methanol and water, from ambient aerosol (PM10) samples, collected over an urban background site in Mumbai, India. The diurnal variability was investigated in samples collected in the morning (7-11 a.m.) and afternoon (12-4 p.m.) periods. Absorption properties of BrC (in the 300-600-nm wavelength range) were measured in filter extracts of water-soluble organic carbon (WSOC) and methanol-soluble organic carbon (MSOC). WSOC and MSOC accounted for on average 52% and 77%, respectively, of the measured OC, potentially indicating unextracted BrC and rendering these values the lower bound. Compared with afternoon samples, the morning samples of MSOC and WSOC had increased BrC concentrations and absorption coefficients (babs365; 40%-65%). The correlation between babs365 and EC, ns-K+, and NO3- in the morning samples indicated contributions from primary sources, including both biomass and vehicular sources. The decreased babs365 in the afternoon samples was partly explained by mixing layer dilution, accompanied by a reduction in the concentrations of primary aerosol constituents. Furthermore, in the afternoon samples, 1HNMR spectroscopy revealed the presence of more oxidized functional groups and significantly higher OC/EC and WSOC/OC ratios, indicating the greater aging of afternoon aerosol. The MAC365 (m2gC-1) for both WSOC and MSOC extracts decreased significantly by 20%-34% in the afternoon samples compared with the morning samples, indicating degradation in the absorption properties of the particles and potentially a change in the constituent BrC chromophores.

Nanoparticle effects on rat alveolar epithelial cell monolayer barrier properties.

Inhaled nanoparticles have been reported to contribute to deleterious effects on human health. In this study, we investigated the effects of ultrafine ambient particulate suspensions (UAPS), polystyrene nanoparticles (PNP; positively and negatively charged; 20, 100, 120 nm), quantum dots (QD; positively and negatively charged; 30 nm) and single-wall carbon nanotubes (SWCNT) on alveolar epithelial cell barrier properties. Transmonolayer resistance (R(t)) and equivalent short-circuit current (I(eq)) of primary rat alveolar epithelial monolayers were measured in the presence and absence of varying concentrations of apical nanoparticles. In some experiments, apical-to-basolateral fluxes of radiolabeled mannitol or inulin were determined with or without apical UAPS exposure and lactate dehydrogenase (LDH) release was analyzed after UAPS or SWCNT exposure. Results revealed that exposure to UAPS decreased R(t) and I(eq) significantly over 24 h, although neither mannitol nor inulin fluxes changed. Positively charged QD decreased R(t) significantly (with subsequent recovery), while negatively charged QD did not. R(t) decreased significantly after SWCNT exposure (with subsequent recovery). On the other hand, PNP exposure had no effects on R(t) or I(eq). No significant increases in LDH release were observed after UAPS or SWCNT exposure. These data indicate that disruption of alveolar epithelial barrier properties due to apical nanoparticle exposure likely involves alteration of cellular transport pathways and is dependent on specific nanoparticle composition, shape and/or surface charge.

Application of land use regression modelling to assess the spatial distribution of road traffic noise in three European cities.

Noise prediction models and noise maps are used to estimate the exposure to road traffic noise, but their availability and the quality of the noise estimates is sometimes limited. This paper explores the application of land use regression (LUR) modelling to assess the long-term intraurban spatial variability of road traffic noise in three European cities. Short-term measurements of road traffic noise taken in Basel, Switzerland (n=60), Girona, Spain (n=40), and Grenoble, France (n=41), were used to develop two LUR models: (a) a "GIS-only" model, which considered only predictor variables derived with Geographic Information Systems; and (b) a "Best" model, which in addition considered the variables collected while visiting the measurement sites. Both noise measurements and noise estimates from LUR models were compared with noise estimates from standard noise models developed for each city by the local authorities. Model performance (adjusted R(2)) was 0.66-0.87 for "GIS-only" models, and 0.70-0.89 for "Best" models. Short-term noise measurements showed a high correlation (r=0.62-0.78) with noise estimates from the standard noise models. LUR noise estimates did not show any systematic differences in the spatial patterns when compared with those from standard noise models. LUR modelling with accurate GIS source data can be a promising tool for noise exposure assessment with applications in epidemiological studies.