The presence of urban greenspace may lead to reduced personal exposure to air pollution via several mechanisms, for example, increased dispersion of airborne particulates; however, there is a lack of real-time evidence across different urban contexts. Study participants were 79 adolescents with asthma who lived in Delhi, India and were recruited to the Delhi Air Pollution and Health Effects (DAPHNE) study. Participants were monitored continuously for exposure to PM₂.₅ (particulate matter with an aerodynamic diameter of less than 2.5 μm) for 48 h. We isolated normal day-to-day walking journeys (n = 199) from the personal monitoring dataset and assessed the relationship between greenspace and personal PM₂.₅ using different spatial scales of the mean Normalised Difference Vegetation Index (NDVI), mean tree cover (TC), and proportion of surrounding green land use (GLU) and parks or forests (PF). The journeys had a mean duration of 12.7 (range 5, 53) min and mean PM₂.₅ personal exposure of 133.9 (standard deviation = 114.8) μg/m³. The within-trip analysis showed weak inverse associations between greenspace markers and PM₂.₅ concentrations only in the spring/summer/monsoon season, with statistically significant associations for TC at the 25 and 50 m buffers in adjusted models. Between-trip analysis also indicated inverse associations for NDVI and TC, but suggested positive associations for GLU and PF in the spring/summer/monsoon season; no overall patterns of association were evident in the autumn/winter season. Associations between greenspace and personal PM₂.₅ during walking trips in Delhi varied across metrics, spatial scales, and season, but were most consistent for TC. These mixed findings may partly relate to journeys being dominated by walking along roads and small effects on PM₂.₅ of small pockets of greenspace. Larger areas of greenspace may, however, give rise to observable spatial effects on PM₂.₅, which vary by season.

Urban environments are characterized by multiple exposures that may influence body mass index (BMI) growth in early life. Previous studies are few, with inconsistent results and no evaluation of simultaneous exposures. Thus, this study aimed to assess the associations between exposure to air pollution, green spaces and built environment characteristics, and BMI growth trajectories from 0 to 5 years. This longitudinal study used data from an electronic primary care health record database in Catalonia (Spain), including 79,992 children born between January 01, 2011 and December 31, 2012 in urban areas and followed until 5 years of age. Height and weight were measured frequently during childhood and BMI (kg/m²) was calculated. Urban exposures were estimated at census tract level and included: air pollution (nitrogen dioxide (NO₂), particulate matter <10 μm (PM₁₀) and <2.5 μm (PM₂.₅₎), green spaces (Normalized Difference Vegetation Index (NDVI) and % green space) and built environment (population density, street connectivity, land use mix, walkability index). Individual BMI trajectories were estimated using linear spline multilevel models with several knot points. In single exposure models, NO₂, PM₁₀, PM₂.₅, and population density were associated with small increases in BMI growth (e.g. β per IQR PM₁₀ increase = 0.023 kg/m², 95%CI: 0.013, 0.033), and NDVI, % of green spaces and land use mix with small reductions in BMI growth (e.g. β per IQR % green spaces increase = −0.015 kg/m², 95%CI: −0.026, −0.005). These associations were strongest during the first two months of life. In multiple exposure models, most associations were attenuated, with only those for PM₁₀ and land use mix remaining statistically significant. This large longitudinal study suggests that early life exposure to air pollution, green space and built environment characteristics may be associated with small changes in BMI growth trajectories during the first years of life, and that it is important to account for multiple exposures in urban settings.

Air pollution is a major global public health problem. The situation is most severe in low- and middle-income countries, where pollution control measures and monitoring systems are largely lacking. Data to quantify the exposure to air pollution in low-income settings are scarce.In this study, land use regression models (LUR) were developed to predict the outdoor nitrogen dioxide (NO₂) concentration in the study area of the Western Region Birth Cohort in São Paulo. NO₂ measurements were performed for one week in winter and summer at eighty locations. Additionally, weekly measurements at one regional background location were performed over a full one-year period to create an annual prediction.Three LUR models were developed (annual, summer, winter) by using a supervised stepwise linear regression method. The winter, summer and annual models explained 52 %, 75 % and 66 % of the variance (R²) respectively. Cross-holdout validation tests suggest robust models. NO₂ levels ranged from 43.2 μg/m³ to 93.4 μg/m³ in the winter and between 28.1 μg/m³ and 72.8 μg/m³ in summer. Based on our annual prediction, about 67 % of the population living in the study area is exposed to NO₂ values over the WHO suggested annual guideline of 40 μg/m³ annual average.In this study we were able to develop robust models to predict NO₂ residential exposure. We could show that average measures, and therefore the predictions of NO₂, in such a complex urban area are substantially high and that a major variability within the area and especially within the season is present. These findings also suggest that in general a high proportion of the population is exposed to high NO₂ levels.

This study combines the methods of observation statistics and remote sensing retrieval, using remote sensing information including the urban heat island (UHI) intensity index, the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the difference vegetation index (DVI) to analyze the correlation between the urban heat island effect and the spatial and temporal concentration distributions of atmospheric particulates in Beijing. The analysis establishes (1) a direct correlation between UHI and DVI; (2) an indirect correlation among UHI, NDWI and DVI; and (3) an indirect correlation among UHI, NDVI, and DVI. The results proved the existence of three correlation types with regional and seasonal effects and revealed an interesting correlation between UHI and DVI, that is, if UHI is below 0.1, then DVI increases with the increase in UHI, and vice versa. Also, DVI changes more with UHI in the two middle zones of Beijing.

Anthropogenic heat emission (AHE) is an important driver of urban heat islands (UHIs). Further, both urban thermal environment research and sustainable development planning require an efficient estimation of anthropogenic heat flux (AHF). Therefore, this study proposed an improved multi-source AHF model, which was constructed using diverse data sources and small-scale samples, to better represent the spatiotemporal distribution of AHF. The performances of three machine learning algorithms (Cubist, gradient boosting decision tree, and simple linear regression) were quantitatively evaluated, and the impact of spatiotemporal heterogeneity on AHF estimation was considered for the first time. The results showed that multi-source datasets and sophisticated algorithms could more effectively reduce the estimation error and improve the accuracy of the spatiotemporal distribution of AHF than simple linear regression. In practical applications, the Cubist model performed better, with prediction errors being less than 0.9 W⋅m−2. Further, the characteristics of different heat sources from the model outputs varied widely, and the building metabolic heat exhibited significant seasonal spatiotemporal variations, which were largely determined by the regional climate. In contrast, industrial and transportation heat showed marginal monthly fluctuations. Similarly, spatiotemporal heterogeneity significantly affected the estimation of building metabolic heat (0.62 W⋅m−2), but it did not affect other heat sources. The proposed improved AHF model was verified to effectively capture the spatiotemporal variations of building heat and solve the issue of overestimation of industrial heat in urban regions. This study provides new methods and ideas for the accurate spatiotemporal quantification of AHF that can supplement future studies on climate warming, UHI, and air pollution.

Heatwaves and greenness have been shown to affect health, but the evidence on their joint effects is limited. We aim to assess the associations of the combined exposure to greenness and heatwaves. We utilized five waves (February 2000–October 2014) of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a prospective cohort of older adults aged 65. We defined heatwaves as the daily maximum temperature ≥92.5th percentile with duration ≥3 days. We calculated the number of heatwave days in one year before death to and cumulative Normalized Difference Vegetation Index (NDVI) during follow-up to assess individual long-term exposure to heatwaves and greenness. Cox proportional hazards models were used to assess the effects of greenness, heatwaves, and their interaction on mortality, adjusted for covariates. We conducted subgroup analyses by residence, gender, and age. There were 20,758 participants in our study, totaling 67,312 person-years of follow-up. The mean NDVI was 0·41 (SD 0.13), and the mean number of heatwave days was 8.92 (2.04). In the adjusted model, the mortality hazard ratio (HR) for each 3-day increase in heatwave days was 1.04 (95% CI 1.04, 1.05), each 0.1-unit decrease in cumulative NDVI was 1.06 (1.05, 1.07). In the adjusted model with an interaction term, the HR for the interaction term was 1.01 (1.01, 1.02) with a p-value less than 0.001. In our subgroup analyses, the HR for each 3-day increase in heatwave days was higher in urban areas than in rural areas (1.06 vs. 1.03), and the HR for 0.1-unit decrease in NDVI was higher in urban areas than in rural areas (1.08 vs. 1.04). Greenness can protect against the effect of heatwaves on mortality, and heatwaves affect the health effects of greenness. Urban dwellers have a higher response to the detrimental effect of heatwaves and a higher marginal benefit from greenness exposure.

Increasingly, studies suggest benefits of natural environments or greenness on children's health. However, little is known about cumulative exposure or windows of susceptibility to greenness exposure. Using inverse probability weighting of marginal structural models (IPW/MSM), we estimated effects of greenness exposure from birth through adolescence on executive function and behavior. We analyzed data of 908 children from Project Viva enrolled at birth in 1999–2002 and followed up until early adolescence. In mid-childhood (median 7.7 years) and early adolescence (13.1 years), executive function and behavior were assessed using the Behavior Rating Inventory of Executive Function and the Strengths and Difficulties Questionnaire (SDQ). Greenness was measured at birth, early childhood, mid-childhood, and early adolescence, using the Normalized Difference Vegetation Index. We used inverse probability weighting of marginal structural models to estimate effects of interventions that ensure maximum greenness exposure versus minimum through all intervals; and that ensure maximum greenness only in early childhood (vs. minimum through all intervals). Results of the effects of “maximum (vs. minimum) greenness at all timepoints” did not suggest associations with mid-childhood outcomes. Estimates of “maximum greenness only in early childhood (vs. minimum)” suggested a beneficial association with mid-childhood SDQ (−3.21, 99 %CI: −6.71,0.29 mother-rated; −4.02, 99 %CI: −7.87,-0.17 teacher-rated). No associations were observed with early adolescent outcomes. Our results for “persistent” maximum greenness exposure on behavior, were not conclusive with confidence intervals containing the null. The results for maximum greenness “only in early childhood” may shed light on sensitive periods of greenness exposure for behavior regulation.

Evidence suggests that residential greenness may be protective of high blood pressure, but there is scarcity of evidence on the associations between greenness around schools and blood pressure among children. We aimed to investigate this association in China. Our study included 9354 children from 62 schools in the Seven Northeastern Cities Study. Greenness around each child’s school was measured by NDVI (Normalized Difference Vegetation Index) and SAVI (Soil-Adjusted Vegetation Index). Particulate matter ≤ 1 μm (PM1) concentrations were estimated by spatiotemporal models and nitrogen dioxide (NO2) concentrations were collected from air monitoring stations. Associations between greenness and blood pressure were determined by generalized linear and logistic mixed-effect models. Mediation by air pollution was assessed using mediation analysis. Higher greenness was consistently associated with lower blood pressure. An increase of 0.1 in NDVI corresponded to a reduction in SBP of 1.39 mmHg (95% CI: 1.86, −0.93) and lower odds of hypertension (OR = 0.76, 95% CI: 0.69, 0.82). Stronger associations were observed in children with higher BMI. Ambient PM1 and NO2 mediated 33.0% and 10.9% of the association between greenness and SBP, respectively. In summary, greater greenness near schools had a beneficial effect on blood pressure, particularly in overweight or obese children in China. The associations might be partially mediated by air pollution. These results might have implications for policy makers to incorporate more green space for both aesthetic and health benefits.

Human disturbance of karst landscapes in tropical volcanic islands present a unique challenge for understanding sediment transport to the coastal zone. Here we present the first evidence of urban drinking water quality impacts from industrial logging in the Solomon Islands. Despite only 6% of the Honiara's drinking water catchment being disturbed by logging, rhodamine dye tracers demonstrated complex karst sinkholes that led to high suspended sediment concentrations being transported from neighbouring Kovi catchment into the Kongulai water supply offtake point for Honiara. This has resulted in the exceedance of practical treatment thresholds of 20 NTU 9.5% of the time, leading to water supply for the majority of Honiara's residents being unavailable for 58 days in 2019. This work highlights the cost-benefit disparity between industrial logging yielding minimal short-term economic yields in comparison to on-going broader impacts of increased coastal sediment transport while restricting water supply to a developing nation's capital.

We quantified mangrove disturbance resulting from Super Typhoon Haiyan using a remote sensing approach. Mangrove areas were mapped prior to Haiyan using 30m Landsat imagery and a supervised decision-tree classification. A time sequence of 250m eMODIS data was used to monitor mangrove condition prior to, and following, Haiyan. Based on differences in eMODIS NDVI observations before and after the storm, we classified mangrove into three damage level categories: minimal, moderate, or severe. Mangrove damage in terms of extent and severity was greatest where Haiyan first made landfall on Eastern Samar and Western Samar provinces and lessened westward corresponding with decreasing storm intensity as Haiyan tracked from east to west across the Visayas region of the Philippines. However, within 18months following Haiyan, mangrove areas classified as severely, moderately, and minimally damaged decreased by 90%, 81%, and 57%, respectively, indicating mangroves resilience to powerful typhoons.