Mohammad Miri

Ecological risk assessment of heavy metal (HM) pollution in the ambient air using a new bio-indicator.

The aim of this descriptive-analytical study was to measure the concentration of heavy metals (HMs) in the leaf and bark of Ulmus carpinifolia as new biological indicators, and the ecological risk assessment of these metals in the ambient air. To achieve these goals, 48 sampling locations were selected in the city and concentration of four HMs—zinc (Zn), copper (Cu), lead (Pb), and cadmium (Cd)—was measured in the mentioned indicator using atomic absorption spectroscopy method. After this, ecological risk assessment, source appointment, and spatial distribution were conducted. In this regard, the enrichment factor (EF), potential ecological risk factor (Er), potential ecological risk index (RI), correlation coefficient (r), and other indices were calculated. The results showed that the concentration of HMs in the leaf and bark in ascending order is as Cd<Cu<Pb<Zn and Cd<Pb<Cu<Zn, respectively. The EF results indicated that the main origin of all measured HMs except Zn is anthropogenic sources. Also, the principal component analysis (PCA) and spatial distribution proved that the concentration of HMs is mainly originated from the traffic and other human activities. On the other hand, the results RI presented that the majority of locations sampled in the study area was exposed to serious ecological risk in terms of surveyed HMs. The leaf and bark of U. carpinifolia can be applied as bio-indicators of the presence of heavy metals in the ambient air and ecological risk imposed by them.

Cutaneous leishmaniasis prevalence and morbidity based on environmental factors in Ilam, Iran: Spatial analysis and land use regression models.

The aim of this study was to investigate the impact of the environmental factors on cutaneous leishmaniasis (CL) prevalence and morbidity in Ilam province, western Iran, as a known endemic area for this disease. Accurate locations of 3237 CL patients diagnosed from 2013 to 2015, their demographic information, and data of 17 potentially predictive environmental variables (PPEVs) were prepared to be used in Geographic Information System (GIS) and Land-Use Regression (LUR) analysis. The prevalence, risk, and predictive risk maps were provided using Inverse Distance Weighting (IDW) model in GIS software. Regression analysis was used to determine how environmental variables affect on CL prevalence. All maps and regression models were developed based on the annual and three-year average of the CL prevalence. The results showed that there was statistically significant relationship (P value≤0.05) between CL prevalence and 11 (64%) PPEVs which were elevation, population, rainfall, temperature, urban land use, poorland, dry farming, inceptisol and aridisol soils, and forest and irrigated lands. The highest probability of the CL prevalence was predicted in the west of the study area and frontier with Iraq. An inverse relationship was found between CL prevalence and environmental factors, including elevation, covering soil, rainfall, agricultural irrigation, and elevation while this relation was positive for temperature, urban land use, and population density. Environmental factors were found to be an important predictive variables for CL prevalence and should be considered in management strategies for CL control.

Mortality and morbidity due to exposure to ambient particulate matter

The aim of this study was to investigate spatial variation and health risk of the exposure to PM2.5 (particulate matter with a diameter of 2.5 µm or less) and PM10 (particulate matter with a diameter of 10 µm or less) in Sabzevar, Iran. PM2.5 and PM10 were measured during three campaigns from April to November 2017, in 26 sampling points. Spatial analysis was performed using kriging and autocorrelations (Morans index) model in Arc GIS software. Relationship between exposure to the PM2.5 and PM10 and their health impacts were investigated by AirQ 2.2.3 software. The mean concentrations (and standard deviation) of PM 2.5 and PM10 over the entire study period were 32.54 (37.28) and 42.61 (47.76) μg/m3, respectively, which were higher than the guideline of World Health Organization. According to the spatial analysis, the maximum concentrations of PM2.5 and PM10 were around the main highway (beltway) which placed all over the south of Sabzevar. According to the Morans index, the emission patterns for PM2.5 (Z-score = 2.53; P-value = 0.011) and PM10 (Z-score = 2.82; P-value = 0.004) were clustered. The attributable percentage (AP) of total mortality related to PM2.5 and PM10 were 3.544% (95% confidence interval (CI): 2.623-4.447%) and 2.055% (95% CI: 1.379-2.721%) per increasing each 10 μg/m3 of these pollutants, respectively. According to observed results, it is suggested that the beltway and other pollution sources, such as industries, should be placed at a greater distance from the city, to reduce PM amounts in residential areas.

A comparative study of an up-flow aerobic/anoxic sludge fixed film bioreactor and sequencing batch reactor with intermittent aeration in simultaneous nutrients (N, P) removal from synthetic wastewater

The performance of two bench scale activated sludge reactors with two feeding regimes, continuous fed (an up-flow aerobic/anoxic sludge fixed film (UAASFF) bioreactor) and batch fed (sequencing batch reactor (SBR)) with intermittent aeration, were evaluated for simultaneous nutrients (N, P) removal. Three significant variables (retention/reaction time, chemical oxygen demand (COD): N (nitrogen): P (phosphorus) ratio and aeration time) were selected for modeling, analyzing, and optimizing the process. At high retention time (≥6 h), two bioreactors showed comparable removal efficiencies, but at lower hydraulic retention time, the UAASFF bioreactor showed a better performance with higher nutrient removal efficiency than the SBR. The experimental results indicated that the total Kjeldahl nitrogen removal efficiency in the UAASFF increased from 70.84% to 79.2% when compared to SBR. It was also found that the COD removal efficiencies of both processes were over 87%, and total nitrogen and total phosphorus removal efficiencies were 79.2% and 72.98% in UAASFF, and 71.2% and 68.9% in SBR, respectively.