The association of exposure to air pollution and depression in women; a cross-sectional study of a middle-income country.

Air pollution exposure has been related with mental disorders, especially depression; however, the available evidence on these associations in Low and Middle-Income Countries (LMICs) is scarce. Therefore, we aimed to assess the relationship between particulate matter (PM) exposure and indicators of traffic with depressive symptoms in women of Sabzevar, Iran. This cross-sectional study was based on 741 women aged 15-45 years (adults) in 2019. The annual average of PM10, PM2.5 and PM1 at home was estimated using land use regression (LUR) models. Street map of Sabzevar was used to calculate the indicators of traffic, i.e., the proximity to major roads and total street length buffers with 100, 300 and 500 m radii around the participants' homes. We used the Center for Epidemiological Studies- Depression (CESD-20) Scale in the general population to measure depression scores. Quasi-Poisson models and logistic regression were used to examine the association and odds ratios of exposure to air pollution and depression scores adjusted for relevant covariates. Exposure to PM10, PM2.5 and PM1 and total street length in 100 m buffer were significantly associated with higher depression scores. In fully adjusted model, an interquartile range (IQR) increase in PM10, PM2.5 and PM1 concentration as well as total street length in 100 m buffer was associated with 1.25 (95% CI:1.03, 1.52, P-value = 0.02), 1.16 (95% CI: 1.06, 1.26, P-value< 0.01), 1.16 (95% CI: 1.03, 1.29, P-Value = 0.01) and 1.15 (95% CI: 1.06, 1.25, P-value< 0.01) odds of clinical depression, respectively. For street length in 300 and 500 m buffers and proximity to major roads, no statistically significant increased risk of clinical depression were observed. Overall, our findings recommended that air pollution exposure has increased the risk of clinical depression in women.

Bioaccumulation and health risk assessment of exposure to potentially toxic elements by consuming agricultural products irrigated with wastewater effluents.

Potentially toxic elements (PTEs) have many adverse health effects due to bioaccumulation capability and their long persistence in the environment. As a renewable water source, the effluents of municipal wastewater treatment systems have been used to irrigate agricultural products widely. However, the evidence on the bioaccumulation of PTEs in crops irrigated with these effluents is still scarce, with no available study in low and middle-income countries. Therefore, this study aimed to assess the PTEs concentration in the soil and crops irrigated with effluents of Sabzevar wastewater treatment plant and the related health risk by that. The clustered method was used to determine the soil and craps samples. Seventy cumulative samples were randomly prepared in summer and autumn 2016 and 2017 from crops, soil and effluent. Inductively coupled plasma mass spectrometry (ICP-MS) was used to measure PTEs. The health risk of exposure to PTEs was assessed using Monte Carlo simulation technique. Kruskal Wallis test and Posthoc Tukey HSD test were used to assess the mean difference of PTEs between soil, effluent and crops as well as between crops together. The bioaccumulation factor (BAF) magnitude order in different crop samples was Cd > Sr > Cu > Pb > Zn > Co > As > Cr > Ni, respectively. The Cd accumulation in Sugar beet plant was significantly higher than in other samples. The highest hazard quotient (HQ) based on single PTEs was observed for As (mineral) (mean: 5.62 × 10-1 and percentile 95th: 2.13) in Okra. Regarding total HQ (THQ), the highest and lowest mean (percentile 95th) values were 1.50 (3.22) and 2.40 × 10-1 (4.01 × 10-1) for Okra and Watermelon, respectively. The mean concentrations of Co, Cr, Ni and Zn were significantly higher in crops compared to soil and influent samples. Posthoc tests indicated that the concentration of PTEs between investigated crop samples were not statistically significant different (p > 0.05). Overall, our study suggested that irrigation with the effluent of stabilization pond wastewater treatment system exerts a potential health risk due to bioaccumulation of PTEs in crops.

Estimate annual and seasonal PM1, PM2.5 and PM10 concentrations using land use regression model.

Exposure to ambient particulate matter (PM) can increase mortality and morbidity in urban area. In this study, annual and seasonal spatial pattern of PM1, PM2.5 and PM10 pollutants were assessed using land use regression (LUR) models in Sabzevar, Iran. The studied pollutants were measured at 26 monitoring stations of different microenvironments in the study area. Sampling was conducted during four campaigns from April 2017 to February 2018. LUR models were developed based on 104 potentially predictive variables (PPVs) subdivided in six categories and 22 sub-categories. The annual mean (standard deviation) of PM1, PM2.5 and PM10 were 36.46 (8.56), 39.62 (10.55) and 51.99 (16.25) µg/m3, respectively. The R2 values and root mean square error for leave-one-out cross validations (RMSE for LOOCV) of PM1 models ranged from 0.23 to 0.79 and 3.43-22.5, respectively. Further, R2 and RMSE for LOOCV of PM2.5 models ranged from 0.56 to 0.93 and 3.66-28.3, respectively. For PM10 models the R2 ranged from 0.31 to 0.82 and the RMSE for LOOCV ranged from 9.16 to 33.9. The generated models can be useful for population based epidemiologic studies and to estimate these pollutants in different parts of the study area for scientific decision making.