Background: Recent epidemiological studies investigating the modifying effect of air temperature in ozone–mortality associations lack consensus as how to adjust for nonlinear and lagged temperature effect in addition to including an interaction term. Methods: We evaluated the influence of temperature confounding control on temperature-stratified ozone–mortality risks in a time series setting in eight European cities and 86 US cities, respectively. To investigate potential residual confounding, we additionally incorporated next day’s ozone in models with differing temperature control. Results: Using only a categorical variable for temperature or only controlling nonlinear effect of low temperatures yielded highly significant ozone effects at high temperatures but also significant residual confounding in both regions. Adjustment for nonlinear effect of temperature, especially high temperatures, substantially reduced ozone effects at high temperatures and residual confounding. Conclusions: Inadequate control for confounding by air temperature leads to residual confounding and an overestimation of the temperature-modifying effect in studies of ozone-related mortality.
Introduction: Traffic and mobile sources are possibly responsible to the most of the ambient volatile organic compounds (VOCs) in urban areas. This study aimed to measure and determine the traffic - related concentrations of benzene, toluene, ethylbenzene, xylenes, formaldehyde, and acetaldehyde at the main streets of Tehran, Iran. Materials and methods: The samples were taken from highly populated streets or main roads with heavy traffic in Central, Northern, Eastern, Southern, and Western areas of Tehran. In total, 33 points for BTEX and 23 points for formaldehyde and acetaldehyde were selected for sampling. The sampling and analysis were performed according to NIOSH methods 1501 and 2016. Results: The averages (± SD) of benzene, toluene, ethylbenzene, xylenes, formaldehyde and acetaldehyde concentrations in Tehran were 15.04 (± 9.18), 23.42 (± 8.73), 4.97 (± 2.55), 11.81 (± 4.46), 107.11 (± 30.58) and 57.10 (± 18.28) ppbv, respectively. Benzene concentrations were 3.30 to 26.00 times higher than air quality standard of European Union (5 μg / m3). High concentrations of BTEX and formaldehyde / acetaldehyde were found in central and eastern areas, respectively. High correlation coefficients were found between BTEX species (r = 0.77-0.95) and also formaldehyde and acetaldehyde (r = 0.98). The highest coefficient of variation (CoV) as a measure of spatial variability was observed for benzene (54.5 %). Conclusion: The high outdoor concentrations observed in this study needs to be decreased immediately, especially in case of benzene.
Introduction: Most vehicles with over 20 years of age have low combustion efficiency by no catalytic converters. One of the most important ways to control and reduce emissions from vehicles is to conduct periodic technical inspection (PTI). According to the regulations of each country, the frequency of periodic technical inspection is different and is determined by the quality of air. Materials and methods: Data of light vehicles, which had been tested in 2010, was collected from Tehran vehicle technical inspection bureau. Collected information of data on vehicles manufactured from 2004 to 2008 including Pride, Peugeot 206, Peugeot 405, Peugeot Pars, Samand and Roa. About 248865 and 236084 vehicles were studied for HC and CO emissions, respectively. Results: According to results one vehicle among every 6 Roa, every 14 Pride, every 22 Peugeot 405, every 40 Peugeot 206, every 125 Samand and every 111 Peugeot Pars, which were referred to technical inspection centers for first time, two years after manufacture, had higher pollutants emission compared to the permitted standards. Conclusion: The results showed that if the periodic technical inspection intervals of vehicles increase from two years to more than two years, for example to five years, about 17.5 % of Peugeot 405 and 10.8 % of pride will be recorded for CO emissions higher than standard rate. Also, about 62 % of new vehicles will have higher levels of HC emissions, which can cause air pollution and an increase in the concentration of pollutants and eventually cause serious harm to public health.
Introduction: Noise pollution has become one of the most serious environmental issues in the large cities of Iran. Although several studies have been conducted on noise pollution in different Iranian cities, to date no such study has been done in Shiraz, a city of roughly 1,250,000. Therefore, the aim of this study was to provide a map of noise pollution in Shiraz and compare the levels of noise pollution in different parts of the city. Materials and methods: For this study, 33 areas were selected in Shiraz. The level of noise pollution was measured at the hours of 008:00, 13:00, 18:00 and 22:00. Then, with the aid of ANOVA, the level of noise pollution in each area was compared to those of other areas of the city. Finally, with the help of ArcView GIS, noise pollution maps of Shiraz were prepared. Results: The results of this study showed that the average noise pollution of Shiraz at the hours of 08:00, 13:00, 18:00 and 22:00 was 58.55, 59.56, 61.1 and 61.07 dB, respectively. On Mondays and Wednesdays, averaging 61.4 and 62 dB, respectively, the highest noise pollution was found in the city of Shiraz. This city’s lowest noise pollution occurred on Friday, Iran’s weekend and day of religious observance, with an average of 56 dB. Conclusion: Some parts of Shiraz at 13:00, 18:00, and 22:00 had higher noise pollution (70 dB). This study was carried out in late summer 2018, so it is recommended that such studies be conducted in other seasons. The results of this study can be used to create a comprehensive noise pollution management plan in Shiraz.
Background/aim The link between particulate matter (PM) exposure and health has been widely explored in epidemiological literature. However, the availability of environmental data is limited to urban settings and little is known about the health effects of PM in rural areas. Our aim is to evaluate a causal association between PM10 and cause-specific mortality in Latium Region (centre Italy) during 2006–2012. Methods We used satellite data combined with spatial predictors in 3-stage mixed models to obtain daily PM10 values on a grid map of 1 × 1 km cells in the Region. For each municipality, we averaged daily PM10 values of each cell in a single yearly value. We used a difference-in-differences approach to estimate a causal relationship between annual PM10 exposure and non-accidental, cardiovascular, and respiratory mortality. We applied Poisson regression models adjusted for municipality and calendar year in order to focus on municipality-levels annual fluctuations of exposure and mortality. In addition, we also added terms for mean and standard deviation (SD) of winter and summer temperatures to account for potential meteorological confounders displaying different temporal trends across municipalities. Results During 2006–2012 we observed 3 47 699 deaths for non-accidental, 92 787 for cardiovascular and 16 509 for respiratory causes in 378 municipalities of Latium Region. The average PM10 concentration during the period was 21.9 mg/m3 (SD 4.9) in Latium. The mean winter and summer temperatures were 11.9, and 17.8°C, while the respective SDs were 2.7°C and 3.9°C. For each IQR (6.8 µg/m3) increase in annual PM10, we estimated a 5.9% (95% CIs: 3.3, 8.5%) increase in non-accidental, 6.2% (1.3, 11.4) cardiovascular, and 17.0% (4.1, 31.5%) in respiratory mortality, respectively. As sensitivity approach, we excluded Rome from the analysis and we found percent increases of risk of 4.2% (1.4, 7.1%) for non-accidental, 4.9% (-0.5, 10.5%) for cardiovascular, and 18.1% (3.8, 34.3%) for respiratory mortality. Conclusion Our analysis suggests a causal effect of PM10 on cause-specific mortality in Latium Region, with a strongest effect on mortality for respiratory causes. We can conclude that PM10 represents a strong risk factor for human health not only in urban settings but also in suburban and rural areas.
