Chromophoric dissolved organic compounds in urban watershed and conventional water treatment process: evidence from fluorescence spectroscopy and PARAFAC.

This study aimed to investigate the origin, quantity, and composition of chromophoric dissolved organic matter (CDOM) from two urbanized watersheds (Cikapundung and Cimahi River), examine how CDOM compounds and absorbances change along the process of two different conventional WTPs (WTP Dago and Cimahi) using PARAFAC, and identify absorbance as potential surrogate parameters for CDOM compounds. Samples were collected from intake, secondary treatment, and filter outlets. PARAFAC was conducted based on two data scenarios: (1) from rainy and dry seasons in Cikapundung river and WTP Dago and (2) from the two rivers and two WTPs during rainy season. Tryptophan-like (C1A) and humic-like (C2A) compounds were identified based on scenario-1 analysis. For scenario-2, humic-like (C1B), peak-M (C2B), and tryptophan-like (C3B) were the main compounds. CDOM compound quantity is consistent with the fluorescence index (FI) and biological index (BIX) which confirmed sewage and animal manure pollution in both watersheds. The best overall removal of CDOM compound occurred in WTP Dago in rainy season. The high concentration of tryptophan-like in Cikapundung River in dry season and in Cimahi River in rainy season has worsen the WTP capability to reduce CDOM. Scenario-1 has shown that in WTP Dago, the potential surrogate parameter for C1A was A240 in rainy season (r = 0.60; p < 0.01) and A410 in dry season (r = - 0.43, p < 0.05). Based on scenario-2, for the WTP Dago in rainy season, C1B strongly correlated with A254 (r = 0.86; p < 0.01), C2B has the strongest correlation with A298 (r = 0.93; p < 0.01), and C3B correlated well with A240 (r = 0.59; p < 0.01). In WTP Cimahi, during rainy season, all compounds correlated well with all measured absorbances, with the strongest correlation with A298.

Long-term Exposure to Low Air Pollutant Concentrations and the Relationship with All-Cause Mortality and Stroke in Older Men.

BACKGROUND: Long-term air pollution exposure has been associated with increased risk of mortality and stroke. Less is known about the risk at lower concentrations. The association of long-term exposure to PM2.5, PM2.5 absorbance, NO2, and NOx with all-cause mortality and stroke was investigated in a cohort of men aged ≥ 65 years who lived in metropolitan Perth, Western Australia. METHODS: Land use regression models were used to estimate long-term exposure to air pollutants at participant's home address (n = 11,627) over 16 years. Different metrics of exposure were assigned: baseline; year before the outcome event; and average exposure across follow-up period. The Mortality Register and Hospital Morbidity Data from the Western Australia Data Linkage System were used to ascertain mortality and stroke cases. Hazard ratios (HRs) and 95% confidence intervals were estimated using Cox proportional hazard models, adjusting for age, smoking, education, and body mass index for all-cause mortality. For fatal and hospitalized stroke, the models included variables controlled for all-cause mortality plus hypertension. RESULTS: Fifty-four percent of all-participants died, 3% suffered a fatal stroke, and 14% were hospitalized stroke cases. PM2.5 absorbance increased the risk of all-cause mortality with adjusted HR of 1.12 (1.02-1.23) for baseline and average exposures, and 1.14 (1.02-1.24) for past-year exposure. There were no associations between PM2.5 absorbance, NO2, and NOx and stroke outcomes. However, PM2.5 was associated with reduced risks of fatal stroke. CONCLUSION: Long-term exposure to PM2.5 absorbance was associated with all-cause mortality among older men exposed to low concentrations; and exposure to PM2.5 was associated with reduced risk of fatal stroke.

The longitudinal association between natural outdoor environments and mortality in 9218 older men from Perth, Western Australia.

BACKGROUND/AIM: Natural outdoor environments may mitigate harmful environmental factors associated with city living. We studied the longitudinal relationship between natural ('green and blue') outdoor environments and mortality in a cohort of older men residing in Perth, Western Australia. METHODS: We studied a cohort of 9218 men aged 65 years and older from the Health In Men Study. Participants were recruited in 1996-99 and followed until 2014, during which 5889 deaths were observed. Time-varying residential surrounding greenness based on the Normalized Difference Vegetation Index, and the number and size of parks, natural space and waterbodies were defined to characterize the natural outdoor environment. All-cause non-accidental and cause-specific mortality was ascertained with the Western Australian Data Linkage System. The association of the natural outdoor environment with mortality was examined using Cox regression analysis. RESULTS: After adjusting for age, men living in the highest quartile of cumulative average surrounding greenness had a 9% lower rate of all-cause non-accidental mortality (95% confidence interval [CI] 0.84, 0.98; p = .013) compared with those in the lowest quartile. This association was no longer present after adjustment for other risk factors, especially level of education. Living within 500 m of one (vs. no) natural space was associated with decreased mortality risk (adjusted hazard ratio 0.93; 95% CI 0.86, 1.00; p = .046), but no association with mortality was found for two or more natural spaces compared to none and for parks. Associations between waterbodies and mortality were inconsistent, showing non-linear beneficial and harmful associations. CONCLUSIONS: In this longitudinal study of older men residing in Perth, we observed evidence suggestive of an association between access to natural spaces and decreased mortality. Associations between surrounding greenness and mortality seemed to be confounded by level of education, and associations with waterbodies were complex and need to be studied further.

Satellite-Based Land-Use Regression for Continental-Scale Long-Term Ambient PM2.5 Exposure Assessment in Australia.

Australia has relatively diverse sources and low concentrations of ambient fine particulate matter (<2.5 µm, PM2.5). Few comparable regions are available to evaluate the utility of continental-scale land-use regression (LUR) models including global geophysical estimates of PM2.5, derived by relating satellite-observed aerosol optical depth to ground-level PM2.5 ("SAT-PM2.5"). We aimed to determine the validity of such satellite-based LUR models for PM2.5 in Australia. We used global SAT-PM2.5 estimates (∼10 km grid) and local land-use predictors to develop four LUR models for year-2015 (two satellite-based, two nonsatellite-based). We evaluated model performance at 51 independent monitoring sites not used for model development. An LUR model that included the SAT-PM2.5 predictor variable (and six others) explained the most spatial variability in PM2.5 (adjusted R2 = 0.63, RMSE (µg/m3 [%]): 0.96 [14%]). Performance decreased modestly when evaluated (evaluation R2 = 0.52, RMSE: 1.15 [16%]). The evaluation R2 of the SAT-PM2.5 estimate alone was 0.26 (RMSE: 3.97 [56%]). SAT-PM2.5 estimates improved LUR model performance, while local land-use predictors increased the utility of global SAT-PM2.5 estimates, including enhanced characterization of within-city gradients. Our findings support the validity of continental-scale satellite-based LUR modeling for PM2.5 exposure assessment in Australia.

Independent Validation of National Satellite-Based Land-Use Regression Models for Nitrogen Dioxide Using Passive Samplers.

Including satellite observations of nitrogen dioxide (NO2) in land-use regression (LUR) models can improve their predictive ability, but requires rigorous evaluation. We used 123 passive NO2 samplers sited to capture within-city and near-road variability in two Australian cities (Sydney and Perth) to assess the validity of annual mean NO2 estimates from existing national satellite-based LUR models (developed with 68 regulatory monitors). The samplers spanned roadside, urban near traffic (≤100 m to a major road), and urban background (>100 m to a major road) locations. We evaluated model performance using R2 (predicted NO2 regressed on independent measurements of NO2), mean-square-error R2 (MSE-R2), RMSE, and bias. Our models captured up to 69% of spatial variability in NO2 at urban near-traffic and urban background locations, and up to 58% of variability at all validation sites, including roadside locations. The absolute agreement of measurements and predictions (measured by MSE-R2) was similar to their correlation (measured by R2). Few previous studies have performed independent evaluations of national satellite-based LUR models, and there is little information on the performance of models developed with a small number of NO2 monitors. We have demonstrated that such models are a valid approach for estimating NO2 exposures in Australian cities.