Associations of greenness, greyness and air pollution exposure with children’s health: a cross-sectional study in Southern Italy

Abstract

BackgroundDue to the complex interplay among different urban-related exposures, a comprehensive approach is advisable to estimate the health effects. We simultaneously assessed the effect of “green”, “grey” and air pollution exposure on respiratory/allergic conditions and general symptoms in schoolchildren.MethodsThis study involved 219 schoolchildren (8–10 years) of the Municipality of Palermo, Italy. Data were collected through questionnaires self-administered by parents and children. Exposures to greenness and greyness at the home addresses were measured using the normalized difference vegetation index (NDVI), residential surrounding greyness (RSG) and the CORINE land-cover classes (CLC). RSG was defined as the percentage of buffer covered by either industrial, commercial and transport units, or dump and construction sites, or urban fabric related features. Two specific categories of CLC, namely “discontinuous urban fabric - DUF” - and “continuous urban fabric - CUF” - areas were found. Exposure to traffic-related nitrogen dioxide (NO2) was assessed using a Land-Use Regression model. A symptom score ranging from 0 to 22 was built by summing affirmative answers to twenty-two questions on symptoms. To avoid multicollinearity, multiple Logistic and Poisson ridge regression models were applied to assess the relationships between environmental factors and self-reported symptoms.ResultsA very low exposure to NDVI ≤0.15 (1st quartile) had a higher odds of nasal symptoms (OR = 1.47, 95% CI [1.07–2.03]). Children living in CUF areas had higher odds of ocular symptoms (OR = 1.49, 95% CI [1.10–2.03]) and general symptoms (OR = 1.18, 95% CI [1.00–1.48]) than children living in DUF areas. Children living in proximity (≤200 m) to High Traffic Roads (HTRs) had increased odds of ocular (OR = 1.68, 95% CI [1.31–2.17]) and nasal symptoms (OR = 1.49, 95% CI [1.12–1.98]). A very high exposure to NO2 ≥ 60 μg/m3 (4th quartile) was associated with a higher odds of general symptoms (OR = 1.28, 95% CI [1.10–1.48]). No associations were found with RGS. A Poisson ridge regression model on the symptom score showed that children living in proximity to HTRs (≤200 m) had a higher symptoms score (RR = 1.09, 95% CI [1.02–1.17]) than children living > 200 m from HTRs. Children living in CUF areas had a higher symptoms score (RR = 1.11, 95% CI [1.03–1.19]) than children living in DUF areas.ConclusionsMultiple exposures related to greenness, greyness (measured by CORINE) and air pollution within the urban environment are associated with respiratory/allergic and general symptoms in schoolchildren. No associations were found when considering the individual exposure to greyness measured using the RSG indicator.Electronic supplementary materialThe online version of this article (10.1186/s12940-018-0430-x) contains supplementary material, which is available to authorized users.

<p>Lifetime Occupational History, Respiratory Symptoms and Chronic Obstructive Pulmonary Disease: Results from a Population-Based Study</p>

“…We failed to explain any association of local differences in air pollution from the traffic with either symptoms or COPD, as both CAT, mMRT scores, and COPD prevalence were similar in those living in the first, second, third or even fifth row of houses from the major road. Outdoor air pollution in studies elsewhere consistently showed greater likelihood of respiratory symptoms in both children and adults [24][25][26][27] and COPD. [8][9][10] We hypothesize, that the basic air pollution level in Almaty is so high, that a distance of 50 to 100 meters from a major road cannot play a role in worsening the symptoms.…”

The effect of residential urban greenness on allergic respiratory diseases in youth: A narrative review

“…Six studies were performed on birth cohorts; 18-23 6 had a cross-sectional design; 18,24-28 1 was an intervention study; 29 and 1 was a case-control study. 30 Twelve studies involved children of age 12 years, [19][20][21][22][23][24][25][26][27][29][30][31] and only two included children and adolescents. 18,28 Most of the studies were conducted in Europe, 19,23,24,27,30 2 in Australia, 21,25,26,28 4 in North America, 18,31 1 in Asia, 29 and 1 based on data from Swedish, Dutch, German, Australian, and Canadian birth cohorts.…”

“…There were 5 different methods for assessing greenness exposure. The Normalized Difference Vegetation Index (NDVI), used by 10 articles [19][20][21][22][23][24][26][27][28]30 , is a validated tool assessing vegetation cover in order to quantify the amount of vegetation derived from publicly available satellite images. NDVI is based on land surface reflectance, ranging from 0 to 1, where 0 means no vegetation and values close to 1 (0.8-0.9) indicate the highest possible density of green leaves.…”

Effects of Particulate Matter on the Incidence of Respiratory Diseases in the Pisan Longitudinal Study

“…There is evidence that air pollution can exacerbate existing respiratory health problems [1,9]; however, whether air pollution may also cause respiratory disease is less certain, since previous studies show conflicting results [10] with limitations on exposure assessment and on the ability to control for individual potential confounders [11]. Recently, the quality of the exposure assessment has been improved, using exposure models that combine data from air quality monitoring stations and satellite data, along with territorial data (e.g., land use) and meteorological parameters [12,13]. These new techniques allow investigating long-term health effects through individual and objective measures of air pollutants exposure.…”