scholarly journals Air Pollution, Noise, Blue Space, and Green Space and Premature Mortality in Barcelona: A Mega Cohort

2018 ◽  
Vol 15 (11) ◽  
pp. 2405 ◽  
Author(s):  
Mark Nieuwenhuijsen ◽  
Mireia Gascon ◽  
David Martinez ◽  
Anna Ponjoan ◽  
Jordi Blanch ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Particulate Matter ◽  
Census Tract ◽  
Vegetation Index ◽  
Green Space ◽  
Road Traffic ◽  
Road Traffic Noise ◽  
Census Tract Level ◽  
All Cause Mortality

Introduction: Cities often experience high air pollution and noise levels and lack of natural outdoor environments, which may be detrimental to health. The aim of this study was to evaluate the effects of air pollution, noise, and blue and green space on premature all-cause mortality in Barcelona using a mega cohort approach. Methods: Both men and women of 18 years and above registered on 1 January 2010 by the Sistema d’Informació pel Desenvolupament de la Investigació en Atenció Primària (SIDIAP) and living in the city of Barcelona were included in the cohort and followed up until 31 December 2014 or until death (n = 2,939,067 person years). The exposure assessment was conducted at the census tract level (n = 1061). We assigned exposure to long term ambient levels of nitrogen dioxides (NO2), nitrogen oxides (NOx), particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5), between 2.5 µm and 10 µm (PM2.5–10, i.e., coarse particulate matter), less than 10 µm (PM10) and PM2.5 light absorption (hereafter referred to as PM2.5 absorbance) based on land use regressions models. Normalized Difference Vegetation Index (NDVI) was assigned based on remote sensing data, percentage green space and blue space were calculated based on land use maps and modelled road traffic noise was available through the strategic noise map for Barcelona. Results: In this large prospective study (n = 792,649) in an urban area, we found a decreased risk of all-cause mortality with an increase in green space measured as NDVI (hazard ratio (HR) = 0.92, 95% CI 0.89–0.97 per 0.1) and increased risks of mortality with an increase in exposure to blue space (HR = 1.04, 95% CI 1.01–1.06 per 1%), NO2 (HR = 1.01, 95% CI 1.00–1.02 per 5 ug/m3) but no risk with noise (HR = 1.00, 95% CI 0.98–1.02 per 5 dB(A)). The increased risks appeared to be more pronounced in the more deprived areas. Results for NDVI, and to a lesser extent NO2, remained most consistent after mutual adjustment for other exposures. The NDVI estimate was a little attenuated when NO2 was included in the model. The study had some limitations including e.g., the assessment of air pollution, noise, green space and socioeconomic status (SES) on census tract level rather than individual level and residual confounding. Conclusion: This large study provides new insights on the relationship between green and blue space, noise and air pollution and premature all-cause mortality.

Reducing cardiovascular health impacts from traffic-related noise and air pollution: intervention strategies

2013 ◽  
Vol 56 (02) ◽  
pp. 31-38 ◽  
Author(s):  
Jason H. Curran ◽  
Helen D. Ward ◽  
Mona Shum ◽  
Hugh W. Davies
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Land Use Planning ◽  
Cardiovascular Health ◽  
Road Traffic ◽  
Traffic Noise ◽  
Scientific Basis ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Road Traffic Noise

Recent studies suggest that exposure to both traffic-related air pollution (TrAP) and to road traffic noise (RTN) are independent risk factors for cardiovascular disease (CVD). While the exact pathophysiologic mechanisms are not known, plausible biological models exist for both associations. This paper describes interventions and mitigating measures aimed at reducing both air and noise pollution emitted from traffic. Nine types of interventions are examined within the four strategic themes of (i) land-use planning and transportation management, (ii) reduction of vehicle emissions, (iii) modification of existing structures, and (iv) behavioral change. Not all interventions result in concomitant reductions of air and noise pollutant exposures. Most interventions that rely on a scientific basis to reduce CVD are directed at reducing TrAP. Interventions identified with the greatest potential benefits focus on the pollutant source, such as reductions in traffic volume and air pollutant emissions, and are more easily realized, and likely cheaper, if they are considered in the land-use planning stages with less reliance on behavioral changes.

scholarly journals Effects of exposure to surrounding green, air pollution and traffic noise with non-accidental and cause-specific mortality in the Dutch national cohort

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jochem O. Klompmaker ◽  
Nicole A. H. Janssen ◽  
Lizan D. Bloemsma ◽  
Marten Marra ◽  
Erik Lebret ◽  
...  
Keyword(s):  
Air Pollution ◽  
Vegetation Index ◽  
Road Traffic ◽  
Traffic Noise ◽  
Ambient Air ◽  
Air Pollutant ◽  
Exposure Model ◽  
Road Traffic Noise ◽  
Exposure Models ◽  
Specific Mortality

Abstract Background Everyday people are exposed to multiple environmental factors, such as surrounding green, air pollution and traffic noise. These exposures are generally spatially correlated. Hence, when estimating associations of surrounding green, air pollution or traffic noise with health outcomes, the other exposures should be taken into account. The aim of this study was to evaluate associations of long-term residential exposure to surrounding green, air pollution and traffic noise with mortality. Methods We followed approximately 10.5 million adults (aged ≥ 30 years) living in the Netherlands from 1 January 2013 until 31 December 2018. We used Cox proportional hazard models to evaluate associations of residential surrounding green (including the average Normalized Difference Vegetation Index (NDVI) in buffers of 300 and 1000 m), annual average ambient air pollutant concentrations [including particulate matter (PM2.5), nitrogen dioxide (NO2)] and traffic noise with non-accidental and cause-specific mortality, adjusting for potential confounders. Results In single-exposure models, surrounding green was negatively associated with all mortality outcomes, while air pollution was positively associated with all outcomes. In two-exposure models, associations of surrounding green and air pollution attenuated but remained. For respiratory mortality, in a two-exposure model with NO2 and NDVI 300 m, the HR of NO2 was 1.040 (95%CI: 1.022, 1.059) per IQR increase (8.3 µg/m3) and the HR of NDVI 300 m was 0.964 (95%CI: 0.952, 0.976) per IQR increase (0.14). Road-traffic noise was positively associated with lung cancer mortality only, also after adjustment for air pollution or surrounding green. Conclusions Lower surrounding green and higher air pollution were associated with a higher risk of non-accidental and cause-specific mortality. Studies including only one of these correlated exposures may overestimate the associations with mortality of that exposure.

scholarly journals Exploring the Effects of Environmental Factors on the Development of 0–4-Year Old Children in The Netherlands

2021 ◽  
Vol 18 (15) ◽  
pp. 7782
Author(s):  
Luuk van Wel ◽  
Paula van Dommelen ◽  
Moniek Zuurbier ◽  
Debbie Heinen ◽  
Jennie Odink ◽  
...  
Keyword(s):  
Air Pollution ◽  
The Netherlands ◽  
Green Space ◽  
Road Traffic ◽  
Traffic Noise ◽  
Age Groups ◽  
Environmental Exposures ◽  
Road Traffic Noise ◽  
Multiple Exposure ◽  
Developmental Milestones

Air pollution, noise, and green space are important environmental exposures, having been linked to a variety of specific health outcomes. However, there are few studies addressing overall early life development. To assess their effects, associations between developmental milestones for a large population of 0–4-year old children in The Netherlands and environmental exposures were explored. Developmental milestones and background characteristics were provided by Preventive Child Health Care (PCHC) and supplemented with data from Statistics Netherlands. Milestones were summarized and standardized into an aggregate score measuring global development. Four age groups were selected. Environmental exposures were assigned to geocoded addresses using publicly available maps for PM2.5, PM10, PMcoarse, NO2, EC, road traffic noise, and green space. Associations were investigated using single and multiple-exposure logistic regression models. 43,916 PCHC visits by 29,524 children were available. No consistent associations were found for air pollution and road traffic noise. Green space was positively associated in single and multiple-exposure models although it was not significant in all age groups (OR 1.01 (0.95; 1.08) (1 year) to 1.07 (1.01; 1.14) (2 years)). No consistent associations were found between air pollution, road traffic noise, and global child development. A positive association of green space was indicated.

scholarly journals Long‐Term Exposure to Air Pollution, Road Traffic Noise, and Heart Failure Incidence: The Danish Nurse Cohort

2021 ◽  
Author(s):  
Youn‐Hee Lim ◽  
Jeanette T. Jørgensen ◽  
Rina So ◽  
Tom Cole‐Hunter ◽  
Amar J. Mehta ◽  
...  
Keyword(s):  
Heart Failure ◽  
Air Pollution ◽  
Particulate Matter ◽  
Cox Regression ◽  
Road Traffic ◽  
Traffic Noise ◽  
Effect Modification ◽  
Road Traffic Noise ◽  
Using Data

Background We examined the association of long‐term exposure to air pollution and road traffic noise with incident heart failure (HF). Methods And Results Using data on female nurses from the Danish Nurse Cohort (aged >44 years), we investigated associations between 3‐year mean exposures to air pollution and road traffic noise and incident HF using Cox regression models, adjusting for relevant confounders. Incidence of HF was defined as the first hospital contact (inpatient, outpatient, or emergency) between cohort baseline (1993 or 1999) and December 31, 2014, based on the Danish National Patient Register. Annual mean levels of particulate matter with a diameter <2.5 µm since 1990 and NO 2 and road traffic noise since 1970 were estimated at participants' residences. Of the 22 189 nurses, 484 developed HF. We detected associations with all 3 pollutants, with hazard ratios (HRs) of 1.17 (95% CI, 1.01–1.36), 1.10 (95% CI, 0.99–1.22), and 1.12 (95% CI, 0.99–1.26) per increase of 5.1 µg/m 3 in particulate matter with a diameter <2.5 µm, 8.6 µg/m 3 in NO 2 , and 9.3 dB in road traffic noise, respectively. We observed an enhanced risk of HF incidence for those exposed to high levels of the 3 pollutants; however, the effect modification of coexposure was not statistically significant. Former smokers and nurses with hypertension showed the strongest associations with particulate matter with a diameter <2.5 µm ( P effect modification <0.05). Conclusions We found that long‐term exposures to air pollution and road traffic noise were independently associated with HF.

scholarly journals Association between Green Space Structure and the Prevalence of Asthma: A Case Study of Toronto

2021 ◽  
Vol 18 (11) ◽  
pp. 5852
Author(s):  
Yuping Dong ◽  
Helin Liu ◽  
Tianming Zheng
Keyword(s):  
Air Pollution ◽  
Vegetation Index ◽  
Green Space ◽  
Normalized Difference Vegetation Index ◽  
Chronic Inflammatory Disease ◽  
Space Structure ◽  
Tree Diversity ◽  
Underlying Mechanisms ◽  
Index Tree

Asthma is a chronic inflammatory disease that can be caused by various factors, such as asthma-related genes, lifestyle, and air pollution, and it can result in adverse impacts on asthmatics’ mental health and quality of life. Hence, asthma issues have been widely studied, mainly from demographic, socioeconomic, and genetic perspectives. Although it is becoming increasingly clear that asthma is likely influenced by green spaces, the underlying mechanisms are still unclear and inconsistent. Moreover, green space influences the prevalence of asthma concurrently in multiple ways, but most existing studies have explored only one pathway or a partial pathway, rather than the multi-pathways. Compared to greenness (measured by Normalized Difference Vegetation Index, tree density, etc.), green space structure—which has the potential to impact the concentration of air pollution and microbial diversity—is still less investigated in studies on the influence of green space on asthma. Given this research gap, this research took Toronto, Canada, as a case study to explore the two pathways between green space structure and the prevalence of asthma based on controlling the related covariates. Using regression analysis, it was found that green space structure can protect those aged 0–19 years from a high risk of developing asthma, and this direct protective effect can be enhanced by high tree diversity. For adults, green space structure does not influence the prevalence of asthma unless moderated by tree diversity (a measurement of the richness and diversity of trees). However, this impact was not found in adult females. Moreover, the hypothesis that green space structure influences the prevalence of asthma by reducing air pollution was not confirmed in this study, which can be attributed to a variety of causes.

scholarly journals Heterogeneous Urban Exposures and Prevalent Hypertension in the Helsinki Capital Region, Finland

2021 ◽  
Vol 18 (3) ◽  
pp. 1196
Author(s):  
Enembe O. Okokon ◽  
Tarja Yli-Tuomi ◽  
Taina Siponen ◽  
Pekka Tiittanen ◽  
Anu W. Turunen ◽  
...  
Keyword(s):  
Antihypertensive Medication ◽  
Green Space ◽  
Road Traffic ◽  
Traffic Noise ◽  
Fine Particulate Matter ◽  
Binary Logistic Regression ◽  
Wood Smoke ◽  
Road Traffic Noise ◽  
Smooth Functions ◽  
Capital Region

Urban dwellers are simultaneously exposed to several environmental health risk factors. This study aimed to examine the relationship between long-term exposure to fine particulate matter (PM2.5, diameter < 2.5 µm) of residential-wood-burning and road-traffic origin, road-traffic noise, green space around participants’ homes, and hypertension. In 2015 and 2016, we conducted a survey of residents of the Helsinki Capital Region to determine their perceptions of environmental quality and safety, lifestyles, and health statuses. Recent antihypertensive medication was used as an indicator of current hypertensive illness. Individual-level exposure was estimated by linking residential coordinates with modelled outdoor levels of wood-smoke- and traffic-related PM2.5, road-traffic noise, and coverage of natural spaces. Relationships between exposure and hypertension were modelled using multi-exposure and single-exposure binary logistic regression while taking smooth functions into account. Twenty-eight percent of the participants were current users of antihypertensive medication. The odds ratios (95% confidence interval) for antihypertensive use were 1.12 (0.78–1.57); 0.97 (0.76–1.26); 0.98 (0.93–1.04) and 0.99 (0.94–1.04) for wood-smoke PM2.5, road-traffic PM2.5, road-traffic noise, and coverage of green space, respectively. We found no evidence of an effect of the investigated urban exposures on prevalent hypertension in the Helsinki Capital Region.

scholarly journals Greenspace, Air Pollution, Neighborhood Factors, and Preeclampsia in a Population-Based Case-Control Study in California

2021 ◽  
Vol 18 (10) ◽  
pp. 5127
Author(s):  
Kari A. Weber ◽  
Wei Yang ◽  
Evan Lyons ◽  
David K. Stevenson ◽  
Amy M. Padula ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Low Income ◽  
Vegetation Index ◽  
Census Data ◽  
Effect Modification ◽  
Severe Preeclampsia ◽  
Quality Data ◽  
Poverty Level ◽  
Neighborhood Factors

To investigate preeclampsia etiologies, we examined relationships between greenspace, air pollution, and neighborhood factors. Data were from hospital records and geocoded residences of 77,406 women in San Joaquin Valley, California from 2000 to 2006. Preeclampsia was divided into mild, severe, or superimposed onto pre-existing hypertension. Greenspace within 100 and 500 m residential buffers was estimated from satellite data using normalized difference vegetation index (NDVI). Air quality data were averaged over pregnancy from daily 24-h averages of nitrogen dioxide, particulate matter <10 µm (PM10) and <2.5 µm (PM2.5), and carbon monoxide. Neighborhood socioeconomic (SES) factors included living below the federal poverty level and median annual income using 2000 US Census data. Odds of preeclampsia were estimated using logistic regression. Effect modification was assessed using Wald tests. More greenspace (500 m) was inversely associated with superimposed preeclampsia (OR = 0.57). High PM2.5 and low SES were associated with mild and severe preeclampsia. We observed differences in associations between greenspace (500 m) and superimposed preeclampsia by neighborhood income and between greenspace (500 m) and severe preeclampsia by PM10, overall and among those living in higher SES neighborhoods. Less greenspace, high particulate matter, and high-poverty/low-income neighborhoods were associated with preeclampsia, and effect modification was observed between these exposures. Further research into exposure combinations and preeclampsia is warranted.

scholarly journals Long-term exposure to ambient air pollution and road traffic noise and asthma incidence in adults: The Danish Nurse cohort

2021 ◽  
Vol 152 ◽  
pp. 106464 ◽  
Author(s):  
Shuo Liu ◽  
Youn-Hee Lim ◽  
Marie Pedersen ◽  
Jeanette T. Jørgensen ◽  
Heresh Amini ◽  
...  
Keyword(s):  
Air Pollution ◽  
Road Traffic ◽  
Traffic Noise ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Road Traffic Noise ◽  
Asthma Incidence

scholarly journals Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda

2020 ◽  
Author(s):  
Eric S. Coker ◽  
Ssematimba Joel ◽  
Engineer Bainomugisha
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Low Cost ◽  
Additive Models ◽  
Pollution Monitoring ◽  
Sub Saharan Africa ◽  
Land Use Regression ◽  
Sub Saharan

Background: There are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods: Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results: Monthly PM2.5 concentration was 60.2 &micro;g/m3 (IQR: 45.4-73.0 &micro;g/m3; median= 57.5 &micro;g/m3). For the ML LUR models, RMSE values ranged between 5.43 &micro;g/m3 - 15.43 &micro;g/m3 and explained between 28% and 92% of monthly PM2.5 variability. Generalized additive models explained the largest amount of PM2.5 variability (R2=0.92) and produced the lowest RMSE (5.43 &micro;g/m3) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion: To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.

scholarly journals Identifikasi Ketersediaan Ruang Terbuka Hijau Kecamatan Kramat Jati Kodya Jakarta Timur Menggunakan Citra Pleiades

2019 ◽  
Vol 16 (2) ◽  
pp. 158
Author(s):  
Mukhoriyah Mukhoriyah ◽  
Nurwita Mustika Sari ◽  
Maya Sharika ◽  
Lidya Nur Hanifati
Keyword(s):  
Land Use ◽  
Open Space ◽  
Vegetation Index ◽  
Green Space ◽  
Rapid Development ◽  
Image Data ◽  
Vegetation Density ◽  
Radiometric Correction ◽  
Key Word ◽  
Density Values

ABSTRACTThe development of big cities in Indonesia especially Jakarta City which is developing very rapidly is marked by the rapid development of physical development, thus affecting the increasing population and land use resulting in a decrease in the amount of vegetation cover. The main problem of the existence of Open Green Space (RTH) in Jakarta is the increasingly reduced / limited land and inconsistencies in implementing spatial planning. The reduced green space is caused by changes in land use that is relatively significant so that green space in Jakarta has not met the target of 30% of the total area, especially in the District of Kramatjati. The purpose of this study is to calculate the need for green space within a district. The method used is the initial data processing (radiometric correction, pancarrage, mosaic, cropping) and calculation of vegetation density values based on Normalized Defference Vegetation Index (NDVI). Based on the results of NDVI calculations using Pleiades Image Data in 2015, that in Kramat Jati Subdistrict there were 225.17 ha as vegetation areas, while 918.93 ha were non-vegetation areas. The results of the calculation are then divided into density levels, ie, a rare density of 48,595 ha, medium density of 34,446 ha, and high density of 160,609 ha. The conclusion obtained is that green open space in Kramat Jati Sub-district is planned to cover 12.38% of the entire Kramat Jati area. However, based on NDVI results, green open space in Kramatjati has reached 19.68% of the entire district area. And  terms of quantity, then the amount of green space has been fulfilled. Key Word : open green space (RTH), Normalized Defference Vegetation Index (NDVI), Pleiades Image ABSTRAKPerkembangan kota-kota besar di Indonesia khususnya Kota Jakarta yang berkembang dengan sangat pesat ditandai perkembangan pembangunan fisik yang cepat, Sehingga mempengaruhi semakin meningkatnya jumlah penduduk dan pemanfaatan lahan yang mengakibatkan berkurangnya jumlah tutupan vegetasi. Permasalahan utama keberadaan Ruang Terbuka Hijau (RTH) di Kota Jakarta adalah semakin berkurangnya/keterbatasan lahan dan ketidak konsisten dalam menerapkan tata ruang. Berkurangnya RTH disebabkan oleh perubahan penggunaan lahan yang relatif signifikan sehingga RTH Jakarta belum memenuhi target 30% dari total luas wilayahnya terutama di Kecamatan Kramatjati. Tujuan dari penelitian ini adalah untuk menghitung kebutuhan RTH dalam satu lingkup kecamatan. Metode yang digunakan adalah pengolahan data awal (koreksi radiometrik, pansharpen, mozaik, cropping) dan perhitungan nilai kerapatan vegetasi berdasarkan Normalized Defference Vegetation Indeks (NDVI). Berdasarkan hasil perhitungan NDVI dengan menggunakan data Citra Pleiades Tahun 2015, bahwa di Kecamatan Kramat Jati terdapat 225,17 ha merupakan daerah vegetasi, sedangkan 918,93 ha adalah daerah non vegetasi. Hasil perhitungan tersebut kemudian di bagi dalam tingkat kerapatan yaitu kerapatan jarang sebesar 48.595 ha, kerapatan menengah sebesar 34.446 ha, dan kerapatan tinggi sebesar 160.609 ha. Kesimpulan yang diperoleh adalah RTH di Kecamatan Kramat Jati direncanakan seluas 12,38 % dari seluruh wilayah Kramat Jati. Namun, berdasarkan hasil NDVI, RTH di Kramatjati sudah mencapai 19,68% dari seluruh luas kecamatan dan dari segi kuantitas, maka jumlah RTH telah terpenuhi.    Kata Kunci: Ruang Terbuka Hijau (RTH), Normalized Defference Vegetation Indeks (NDVI), Citra Pleiades

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