Dataset Information

Annual and seasonal spatial models for nitrogen oxides in Tehran, Iran.

ABSTRACT: Very few land use regression (LUR) models have been developed for megacities in low- and middle-income countries, but such models are needed to facilitate epidemiologic research on air pollution. We developed annual and seasonal LUR models for ambient oxides of nitrogen (NO, NO2, and NOX) in the Middle Eastern city of Tehran, Iran, using 2010 data from 23 fixed monitoring stations. A novel systematic algorithm was developed for spatial modeling. The R(2) values for the LUR models ranged from 0.69 to 0.78 for NO, 0.64 to 0.75 for NO2, and 0.61 to 0.79 for NOx. The most predictive variables were: distance to the traffic access control zone; distance to primary schools; green space; official areas; bridges; and slope. The annual average concentrations of all pollutants were high, approaching those reported for megacities in Asia. At 1000 randomly-selected locations the correlations between cooler and warmer season estimates were 0.64 for NO, 0.58 for NOX, and 0.30 for NO2. Seasonal differences in spatial patterns of pollution are likely driven by differences in source contributions and meteorology. These models provide a basis for understanding long-term exposures and chronic health effects of air pollution in Tehran, where such research has been limited.

SUBMITTER: Amini H

PROVIDER: S-EPMC5020732 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Annual and seasonal spatial models for nitrogen oxides in Tehran, Iran.

Amini Heresh H Taghavi-Shahri Seyed-Mahmood SM Henderson Sarah B SB Hosseini Vahid V Hassankhany Hossein H Naderi Maryam M Ahadi Solmaz S Schindler Christian C Künzli Nino N Yunesian Masud M

Scientific reports 20160913

Very few land use regression (LUR) models have been developed for megacities in low- and middle-income countries, but such models are needed to facilitate epidemiologic research on air pollution. We developed annual and seasonal LUR models for ambient oxides of nitrogen (NO, NO2, and NOX) in the Middle Eastern city of Tehran, Iran, using 2010 data from 23 fixed monitoring stations. A novel systematic algorithm was developed for spatial modeling. The R(2) values for the LUR models ranged from 0.6 ...[more]

PMID: 27622593

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Project description:Land-use regression (LUR) models have been developed to estimate spatial distributions of traffic-related pollutants. Several studies have examined spatial autocorrelation among residuals in LUR models, but few utilized spatial residual information in model prediction, or examined the impact of modeling methods, monitoring site selection, or traffic data quality on LUR performance. This study aims to improve spatial models for traffic-related pollutants using generalized additive models (GAM) combined with cokriging of spatial residuals. Specifically, we developed spatial models for nitrogen dioxide (NO(2)) and nitrogen oxides (NO(x)) concentrations in Southern California separately for two seasons (summer and winter) based on over 240 sampling locations. Pollutant concentrations were disaggregated into three components: local means, spatial residuals, and normal random residuals. Local means were modeled by GAM. Spatial residuals were cokriged with global residuals at nearby sampling locations that were spatially auto-correlated. We compared this two-stage approach with four commonly-used spatial models: universal kriging, multiple linear LUR and GAM with and without a spatial smoothing term. Leave-one-out cross validation was conducted for model validation and comparison purposes. The results show that our GAM plus cokriging models predicted summer and winter NO(2) and NO(x) concentration surfaces well, with cross validation R(2) values ranging from 0.88 to 0.92. While local covariates accounted for partial variance of the measured NO(2) and NO(x) concentrations, spatial autocorrelation accounted for about 20% of the variance. Our spatial GAM model improved R(2) considerably compared to the other four approaches. Conclusively, our two-stage model captured summer and winter differences in NO(2) and NO(x) spatial distributions in Southern California well. When sampling location selection cannot be optimized for the intended model and fewer covariates are available as predictors for the model, the two-stage model is more robust compared to multiple linear regression models.

Project description:BACKGROUND:With increasing frequencies of non-fatal overdose in people who inject drugs (PWID), it is essential to improve our knowledge about associated risk factors for overdose to inform overdose prevention and assistance programs. The aim of present study was to determine the prevalence of non-fatal overdose and the associated risk factors among PWID in Tehran, Iran. METHODS:Snowball sampling was used to collect data from 465 participants in Tehran using a cross-sectional survey. Consenting participants who reported drug injecting in the past month and were able to speak and comprehend Farsi enough to respond to survey questions were interviewed. The endpoint of interest was non-fatal overdose in the previous 6 months, or answering "Yes" to the question: "In the last six months, have you ever overdosed by accident? (at least once)". We used STATA v. 14 for this analysis. Statistical significance was defined as p?<?0.05 for all analyses. RESULTS:Of 465 PWIDs who participated in this study, all were male, and about half had less than a high school education. The prevalence of self-reported non-fatal overdose in the past 6?months was 38% (CI95%: 34, 43%). Our findings indicate that characteristics and behaviors that were associated with an increased risk of experiencing an overdose in the past 6 months were drug use initiation under 22?years (AOR =2.2, P?<?0.05), using methamphetamine (AOR =2.8, P?< 0.05), and using multiple drugs at the same time (AOR =2.1, P?< 0.05). Also, more recent initiates to injecting (<?2?years) had an increased risk of experiencing an overdose in the past 6 months. The odds of experiencing a non-fatal overdose among PWIDs who regularly attended NSP were 0.6 times less than for those who did not attend regularly (OR?=?0.6,95% CI: 0.2-0.9). CONCLUSION:Methamphetamine and alcohol use were the most significant association for non-fatal overdose among PWIDs. Our results indicate that intervention and prevention initiatives seeking to reduce overdoses among PWIDs should not only be focused on the primary drug used but also the use of alcohol and poly-drug use.

Dataset Information

Annual and seasonal spatial models for nitrogen oxides in Tehran, Iran.

Publications

Annual and seasonal spatial models for nitrogen oxides in Tehran, Iran.

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