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Background: Management and cleaning of large environmental monitored data sets is a specific challenge. In this article, the authors present a novel framework for exploring and cleaning large datasets. As a case study, we applied the method on air quality data of Tehran, Iran from 1996 to 2013. , Methods: The framework consists of data acquisition [here, data of particulate matter with aerodynamic diameter ≤10 µm (PM10)], development of databases, initial descriptive analyses, removing inconsistent data with plausibility range, and detection of missing pattern. Additionally, we developed a novel tool entitled spatiotemporal screening tool (SST), which considers both spatial and temporal nature of data in process of outlier detection. We also evaluated the effect of dust storm in outlier detection phase., Results: The raw mean concentration of PM10 before implementation of algorithms was 88.96 µg/m3 for 1996-2013 in Tehran. After implementing the algorithms, in total, 5.7% of data points were recognized as unacceptable outliers, from which 69% data points were detected by SST and 1% data points were detected via dust storm algorithm. In addition, 29% of unacceptable outlier values were not in the PR.  The mean concentration of PM10 after implementation of algorithms was 88.41 µg/m3. However, the standard deviation was significantly decreased from 90.86 µg/m3 to 61.64 µg/m3 after implementation of the algorithms. There was no distinguishable significant pattern according to hour, day, month, and year in missing data., Conclusion: We developed a novel framework for cleaning of large environmental monitored data, which can identify hidden patterns. We also presented a complete picture of PM10 from 1996 to 2013 in Tehran. Finally, we propose implementation of our framework on large spatiotemporal databases, especially in developing countries.

Purpose: This study aimed to investigate chemical composition of PM10 (particulate matter with aerodynamic diameter ≤ 10 μm) during dust storm and inversion in Tehran and hemolysis effects. Methods: PM10 was sampled in Tehran, Iran, during dust storm and inversion conditions. Water soluble ions (F¯, Cl¯, NO2¯, NO3¯, SO4¯2, Na+, K+, NH4+, Ca+2, Mg+2) and elements (Al, Ba, Cd, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Pb, Se, Sn, Sr, V, Zn, Pt, Rh, Pd, As and Si) were analyzed by ion chromatograph (IC) and inductively coupled plasma optical emission spectrometer (ICP-OES), respectively. Hemolysis was examined as in vitro at PM10 concentrations of 50–300 μg/ml. Results: Daily average of PM10 concentrations in dusty and inversion days were 348.40 and 220.54 μg/m3, respectively. Most prevalence ionic components were NO3¯, Cl¯, SO4¯2 and Ca+2 during dust storm and SO4¯2, NO3¯, Cl¯ and NH4+ during inversion. Si, Fe and Al had the maximum values in both conditions. Particles associated with both conditions induced hemolytic responses. PM10 from dusty day showed a higher hemolysis percent (10.24 ± 4.67%) than inversion (9.08 ± 5.47%), but this difference was not significant (p = 0.32). Hemolytic effects were significantly intensified by increased PM concentrations (p < 0.001) in a dose-response manner. Conclusions: As the results, chemical composition of sampled particles from inversion days and dust storm was different from each other. Hemolytic effects of particles during dust storm were more than inversion days. However, this difference was not statistically significant. [ABSTRACT FROM AUTHOR]

Background: Polychlorinated dibenzo-p-dioxins (PCDD) and dibenzofurans (PCDFs) are highly toxic persistent organic pollutants (POPs), which can cause various health outcomes, such as cancer. As a part of the National and Sub-national Burden of Disease Study (NASBOD), we aimed to estimate dioxins and furans national emissions, identify their main sources, estimate daily intake doses, and assess their trend from 1990-2010 in Iran. Methods: The Toolkit for Identification and Quantification of Releases of Dioxins, Furans and Other Unintentional POPs, which is developed by the United Nations Environment Programme (UNEP 2013), was used to estimate the emissions of PCDD/PCDFs from several sources into the air, water, land, residue, and other products. The daily intake doses were estimated using a linear regression of estimated emissions by UNEP Toolkit and average intake doses in other countries. Finally, the trend of PCDD/PCDFs emissions and daily intake doses were explored from 1990-2010. Results: The total emissions were estimated as 960 g Toxic Equivalents (g TEQ) for 1990 and 1957 g TEQ for 2010 (18.2 and 26.8 g TEQ per million capita, respectively). The estimations suggest that albeit contribution of open burning to PCDD/PCDFs emissions has been declining from 1990 to 2010, it remained the major source of emissions in Iran contributing to about 45.8% out of total emissions in 1990 to 35.7% in 2010. We further found that PCDD/PCDFs are mostly emitted into the ambient air, followed by residue, land, products, and water. The daily intake doses were estimated to be 3.1 and 5.4 pg TEQ/kg bw/day for 1990 and 2010, respectively. We estimated an increasing trend for PCDD/PCDFs emissions and intake doses in Iran from 1990-2010. Conclusions: The high levels of emissions, intake doses, and their increasing trend in Iran may pose a substantial health risk to the Iranian population. Further studies with more rigorous methods are recommended but this should not circumvent taking appropriate policy actions against these pollutants. Currently, Iran has no standard for dioxins and furans. Adaptation of the World Health Organization recommended guidelines might be an appropriate starting point to control dioxins and furans emissions. [ABSTRACT FROM AUTHOR]

The primary data about the role of automotive manufacturing industry in emitting dichloromethane (DCM) to the environment in Iran are provided by a case study of SAIPA Automotive Manufacturing Company in 2012. The average emission rate and emission factor of DCM in the stack exhaust air streams of the solvent-based automotive painting plant were 6.8 kg·d−1and 19.5 g per car, respectively. The spray booths had the highest portion (about 85%) of DCM emission in the stack exhaust air streams. The average concentration of DCM (± standard deviation) in the generated wastewater of the factory was 64 ± 12 μg·L−1, but in the effluent of the factory wastewater treatment plant, it was reduced to a nondetectable level. DCM was also observed in three groundwater wells out of five monitored water resources. To control DCM emission and prevent pollution, the replacement of solvent-based paints with water-based paints is highly recommended. [ABSTRACT FROM AUTHOR]

Air pollution has been identified as one of the most challenging health issues in urban areas worldwide. The aim of this study was to investigate the association between short-term exposure to ambient air pollution and respiratory disease over a long-term period in Shiraz, one of the largest cities in Iran. Methods: hospital admissions due to respiratory diseases (asthma, pneumonia, chronic obstructive pulmonary disease (COPD) and pleural effusion) in residents of Shiraz between March 21, 2009 and March 20, 2015 were included. Demographics for each patient and meta data to include principal meteorological variables (temperature and relative humidity) and five ambient pollutants (CO, O 3 , SO 2 , NO 2 , and PM 10) were also included. Statistical analysis was performed by Poisson regression in single-pollutant generalized linear model with principal component analysis (GLPCA) to analyze the relationship between pollutants and hospital admissions at the 0–9 cumulative lag day period. Pearson correlation test was used to determine the relationship between different pollutants, temperature and humidity. It was found that the highest increase in asthma admission was related to PM 10 (relative risk (RR) = 1.31, 95% CI = 1.17, 1.47). For COPD, the rate of hospital visits significantly increased with the increase in NO 2 concentration (RR = 1.17, 95% CI = 1.09.1.27). In the children's hospital, O 3 (RR = 1.25, 95% CI = 1.06, 1.47) and SO 2 (RR = 1.17, 95% CI = 1.07, 1.28) affected the asthma admissions and all contaminants (highest RR observed was for NO 2 (RR = 1.28 95% CI = 1.18, 1.40) affected pneumonia admissions on cumulative lag days of 0–9. Conclusions: These data confirm an association between ambient air pollutants and hospital admissions due to respiratory disease. Image 1 • The first air pollution and respiratory hospital admission study PCA model in world. • The first air pollution and respiratory duration of hospitalization study PCA model in world. • The study site in particular is regarded as one of the dustiest areas worldwide and thus this study is very important. • Respiratory hospital admissions in Shiraz increased positively with short-term exposure to PM 10 , CO, NO 2, O 3. • PM 10 has the strongest impact. [ABSTRACT FROM AUTHOR]

Abstract Scientists have considered epigenetic modifications as a possible mechanism to deal with adverse effects of air pollution. This study aimed to compare the effect of PM 10 (PM with aerodynamic diameter ≤ 10 μm) from dust storm and inversion conditions on in vitro global methylation in human peripheral blood mononuclear cells (PBMCs). PM 10 was sampled in Tehran, Iran, at a point impacted with dust storm and inversion. PM toxicity was determined using the MTT assay. PBMCs were extracted from whole blood of healthy males and treated separately with a mixture of pooled PM 10 from inversion and dusty conditions at concentrations of 50–300 μg/mL for 4 h. Untreated cells were used as the negative control. Moreover, 5-methylsytosine (%5-mC) and 5-hydroxymethylcytosine (%5-hmC) were measured by the enzyme-linked immunosorbent assay (ELISA) method. Daily average PM 10 concentrations in dusty and inversion days were 348.40 and 220.54 μg/m3, respectively. The mean of %5-mC (2.04 ± 1.49%) was estimated 12 times more than that of %5-hmC (0.17 ± 0.11%). PM 10 resulting from the both sources caused DNA hypomethylation; however, this effect from inversion (median = 3%, IQR = 2.4%) was found to be significantly more than that from dust storm (median = 1.1%, IQR = 1.38%). Moreover, particles increased %5-hmC caused by PM 10 , which was significantly greater when resulting from inversion (0.23 ± 0.1%) than from dust storm (0.12 ± 0.09%). Furthermore, %5-mC and %5-hmC were significantly different at different PM 10 concentrations (50–300 μg/mL) so that a significant difference was observed between %5-mC and %5-hmC at extreme concentrations. Results showed that PM 10 from inversion caused a significantly more global methylation than that from dust storm. It can be concluded that measurement of 5-mC and 5-hmC as epigenetic modifications in environmental studies of DNA methylation can be a good procedure to determine health effects related to PM 10 exposure. Highlights • Effect of airborne particles investigated on global DNA methylation. • PM 10 collected in two air pollution conditions, dust storm and inversion, in Tehran. • DNA methylation measured through analyzing %5-mC and %5-hmC in human PBMCs in vitro. • Exposure of PBMCs to PM 10 caused a decreased %5-mC and increased %5-hmC. • Effects through PM 10 associated with inversion conditions were higher than dust storm. [ABSTRACT FROM AUTHOR]