The current study aims at modelling the dispersion of two pollutants, namely CO (carbon monoxide) and SO2 (sulfur dioxide) released from District 7 of Tehran Municiaplity, from 20 main line sources, by means of CALPUFF modeling system. CALPUFF is a non-steady state puff modeling software which employs meteorological, terrain, and land-use data to effectively simulate air pollutants' dispersion from a given source. CALMET software has been applied to provide meteorological conditions within the study domain. The study has been carried out on September 30, 2012 and shows that the modeled concentrations have been below both Iranian air ambient standard and NAAQS standard for CO and SO2. It also compares the measurements from the monitoring station of Setad Bohran, showing that the simulated hourly mean concentrations of the SO2 and CO do not follow similar temporal patterns for measurement values. For the absolute value, model results seem to be highly underestimated, compared to the monitored data (R2 = -0.41).

The present study analyzes air quality for Carbon monoxide (CO), in Esfahan with the measurements taken in three different locations to prepare average data in the city. The average concentrations have been measured every 24 hours, every month and every season with the results showing that the highest concentration of CO occurs generally in the morning and at the beginning of night, while the least concentration has been found in the afternoon and early morning. Monthly concentrations of CO show the highest values in August and the lowest values in February. The seasonal concentrations show the least amounts in spring, while the highest amounts belong to summer. Relations between the air pollutant and some meteorological parameters have been calculated statistically, using the daily average data. The data include Temperature (min, max), precipitation, Wind Direction (max), Wind Speed (max), and Evaporation, considered independent variables. The relations between the pollutant concentration and meteorological parameters have been expressed by multiple linear regression equations for both annual and seasonal conditions, using SPSS software. Analysis of variance shows that both regressions of ‘enter’ and ‘stepwise’ methods are highly significant, indicating a significant relation between the CO and different variables, especially for temperature and wind speed in annual condition. RMSE test shows that among different prediction models, stepwise model is the best option.

It has been more than 20 years that the Measurement of Pollution in The Troposphere (MOPITT) mission onboard the NASA Terra satellite keeps providing us CO atmospheric concentration measurements around the globe. The current paper observes CO mixing ratio from the MOPITT Version 8 (MOP03J_V008) instrument in order to study the spatiotemporal analysis of CO (spanning from April 2000 to February 2020) in the Troposphere of Iran. Results indicate that the average CO in Iran’s troposphere has been 133.5 ppbv (i.e., 5.5 ppbv lower than the global mean CO). The highest distribution of CO (with an average of 150 ppbv) belongs to the city of Tehran (the capital of Iran) as well as the Caspian Sea coastal area, while the lowest value (with an average of less than 110 ppbv) has been estimated on the Zagros Mountains (southwestern Iran). The highest and lowest CO values have been observed in cold and hot months, respectively. Seasonally speaking, it is also clear that the highest and lowest carbon monoxide values occur in winter and summer, respectively. The vertical profile of MOPITT CO shows the maximum CO concentration at lower levels of the troposphere. It has been expanded up to 150 hPa. The trend is investigated by means of Pearson correlation coefficient statistical method. Overall, long-term monitoring of MOPITT CO in Iran indicates a decreasing trend of tropospheric CO over the 20 years (Y=-0.008X+449.31). Possible reasons for such a decrease can be related to improved transportation fleet, increased fuel quality, plans for traffic control, promotion of heating systems, and promotion of industrial fuels and factories.

The emission factor (EF) is a parameter used to assess vehicle emissions. Many studies have reported EFs for vehicles in driving conditions. However, the idling emissions should not be neglected in characterizing actual vehicle emissions in congested large cities, where idling is very common on the road. Whereas, EF data for idling vehicles have scarcely been reported in the literature, let alone comparison of different fuels. In this study, the EFs of passenger cars burning four types of fuels - gasoline, compressed natural gas (CNG), diesel, and liquefied petroleum gas (LPG) were measured and compared. The emissions data for CO, CO₂, unburned hydrocarbon (HC), and NO were recorded to calculate fuel-based EFs in units of g pollutants/kg fuel burned. EFs for CO, HC, and NO were compared for the four fuels. Diesel vehicles had the highest EF for CO, with an average value of 35.12 ± 21.37 g/kg fuel, due to low concentration of CO₂ in lean operation compared to CO emission. CNG vehicles had the highest EF for HC, with an average value of 28.15 ± 11.97 g/kg fuel, due to high concentration of unburned methane gas due to slow CNG flame speed whereas diesel vehicles again had the highest EF for NO due to high temperature and pressure and freezing of NO decomposition reaction, with an average value of 12.07 ± 5.37 g/kg fuel. Further comparison was conducted to analyze the effects of two additional variables on EF: engine displacement volume and model/brand year. Only the gasoline-fueled vehicles showed an increase in EFs (for CO, HC and NO) with the vehicle age according to the model year. However, no clear correlation was observed for CNG, LPG, and diesel-fueled vehicles. Finally, the EF results were compared with those reported in the literature, which have been measured in various countries under both idling and non-idling conditions. Because the idling EFs were not substantially smaller than those under moving conditions, and vehicles spend substantial time idling in large cities, idling emissions should not be ignored in the emission inventories for large cities.

Studies of algal bloom early warning systems have rarely paid attention to the dynamics of excessive proliferation of phytoplankton (EPP), which occurs prior to algal blooms, or to the sensitivity of a lake to EPP based on multiple environmental factors. In this study, we investigated EPP dynamics in large lakes and identified major factors that influenced the lake's vulnerability to EPP, to improve algal bloom early warning systems. High temporal moderate resolution imaging spectroradiometer (MODIS) images and multi-source daily site monitoring data of large lakes in the middle–lower Yangtze River basin were analyzed. Then, the floating algal index (FAI) and resource use efficiency (RUE) by phytoplankton were used to investigate the EPP dynamics and lake's vulnerability to EPP, respectively. Moreover, generalized linear models were used to assess the relative importance of environmental factors on RUE. The results indicate that the lakes freely connected (FC) to the Yangtze River (Dongting Lake and Poyang Lake) had lower FAIs but higher RUEs than the non-connected lakes (NC; Chaohu Lake and Taihu Lake). The key factors affecting RUE-FC were standard deviation of water level within 30 days(WL30), particulate matter <10 μm(PM₁₀), and relative humidity(Hum), which explained 15.91% of the variations in RUE. The key factors affecting RUE-NC were ozone(O₃), basin normalized difference vegetation index standard deviation(BNDVISD), and dissolved oxygen(DO), which explained 35.28% of the variations in RUE. These results emphasize the importance of air quality in influencing or reflecting EPP risks in large lakes. In addition, basin vegetation and hydrological rhythms can influence NH₄⁺ through non-point source loading. Algal bloom early warning systems can be improved by routine monitoring and forecasting of potential environmental factors such as air quality and basin vegetation.

Numerous epidemiological studies have indicated the adverse cardiovascular effects of air pollution on heart failure (HF) risk. However, little data are available directly evaluating the association of ambient volatile organic compounds (VOCs) with HF risk. We aimed to estimate the short-term effects of ambient VOCs on HF emergency hospitalizations in Hong Kong and to evaluate whether the associations were modified by sex and age.We collected the daily VOCs concentrations from the Hong Kong Environmental Protection Department between April 2011 to December 2014. HF emergency hospital admission data were obtained from the Hospital Authority of Hong Kong. Generalized additive model (GAM) integrated with the distributed lag model (DLM) was used to estimate the excess risks of HF emergency hospitalizations with ambient concentrations of each VOCs groups – alkane, alkene, alkyne, benzene and substituted benzene.We observed short-term effects of alkyne and benzene on an increased risk of HF emergency hospitalizations. The cumulative effect over 0–6 lag days (dlm₀₋₆) for an IQR increment of alkyne (1.17 ppb) was associated with 4.2% (95% CI: 1.18%–7.26%) increases of HF emergency hospitalizations, while the corresponding effect estimate over dlm₀₋₂ for benzene per IQR (0.43 ppb) was 2.7% (95% CI: 0.39%–5.04%). Each VOCs groups was significantly associated with HF emergency hospitalizations in men.Ambient volatile organic compounds, particularly alkyne and benzene, were associated with increased risks of heart failure in the Hong Kong population.

Adverse effect studies of gasoline exhaust are scarce, even though gasoline direct injection (GDI) vehicles can emit a high number of particles.The aim of this study was to conduct an in vitro hazard assessment of different GDI exhausts using two different cell culture models mimicking the human airway. In addition to gasoline particle filters (GPF), the effects of two lubrication oils with low and high ash content were assessed, since it is known that oils are important contributors to exhaust emissions.Complete exhausts from two gasoline driven cars (GDI1 and GDI2) were applied for 6 h (acute exposure) to a multi-cellular human lung model (16HBE14o-cell line, macrophages, and dendritic cells) and a primary human airway model (MucilAir™). GDI1 vehicle was driven unfiltered and filtered with an uncoated and a coated GPF. GDI2 vehicle was driven under four settings with different fuels: normal unleaded gasoline, 2% high and low ash oil in gasoline, and 2% high ash oil in gasoline with a GPF. GDI1 unfiltered was also used for a repeated exposure (3 times 6 h) to assess possible adverse effects.After 6 h exposure, no genes or proteins for oxidative stress or pro-inflammation were upregulated compared to the filtered air control in both cell systems, neither in GDI1 with GPFs nor in GDI2 with the different fuels. However, the repeated exposure led to a significant increase in HMOX1 and TNFa gene expression in the multi-cellular model, showing the responsiveness of the system towards gasoline engine exhaust upon prolonged exposure.The reduction of particles by GPFs is significant and no adverse effects were observed in vitro during a short-term exposure. On the other hand, more data comparing different lubrication oils and their possible adverse effects are needed. Future experiments also should, as shown here, focus on repeated exposures.

One of the fundamentals in the ecotoxicological studies is the need of data comparison, which can be easily reached with the help of a standardized biological model. In this context, any biological model has been still proposed for the biomonitoring and risk evaluation of freshwaters until now. The aim of this review is to illustrate the ecotoxicological studies carried out with the zebra mussel Dreissena polymorpha in order to suggest this bivalve species as possible reference organism for inland waters. In detail, we showed its application in biomonitoring, as well as for the evaluation of adverse effects induced by several pollutants, using both in vitro and in vivo experiments. We discussed the advantages by the use of D. polymorpha for ecotoxicological studies, but also the possible limitations due to its invasive nature.

Growing epidemiological evidence has shown that exposure to ambient air pollution contributes to poor sleep quality. However, whether variability in air pollution exposure affects sleep quality remains unclear. Based on a large sample in China, this study linked individual air pollutant exposure levels and temporal variability with subjective sleep quality. Town-level data on daily air pollution concentration for 30 days prior to the survey date were collected, and the monthly mean value, standard deviations, number of heavily polluted days, and trajectory for six common pollutants were calculated to measure air pollution exposure and its variations. Sleep quality was subjectively assessed using the Pittsburgh Sleep Quality Index (PSQI), and a PSQI score above 5 indicated overall poor sleep quality. Multilevel and negative control models were used. Both air pollution exposure and variability contributed to poor sleep quality. A one-point increase in the one-month mean concentration of particulate matter with aerodynamic diameters of ≤2.5 μm (PM₂.₅) and ≤10 μm (PM₁₀) led to 0.4% (95% confidence interval (CI): 1.002–1.006) and 0.3% (95% CI: 1.001–1.004) increases in the likelihoods of overall poor sleep quality (PSQI score >5), respectively; the odds ratios of a heavy pollution day with PM₂.₅ and PM₁₀ were 2.2% (95% CI: 1.012–1.032) and 2.2% (95% CI: 1.012–1.032), respectively. Although the mean concentrations of nitrogen dioxide, sulfur dioxide, and carbon monoxide met the national standard, they contributed to the likelihood of overall poor sleep quality (PSQI score >5). A trajectory of air pollution exposure with maximum variability was associated with a higher likelihood of overall poor sleep quality (PSQI score >5). Subjective measures of sleep latency, duration, and efficiency (derived from PSQI) were affected in most cases. Thus, sleep health improvements should account for air pollution exposure and its variations in China under relatively high air pollution levels.

Black carbon (BC) and carbon monoxide (CO) at different model levels from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis were comprehensively evaluated against observations performed simultaneously on both surface and mountain sites in winter and summer in the North China Plain for the first time. CAMS could capture the seasonal difference in BC and CO emission on both sites but showed significant and persistent biases. Biases were high on the surface site and low on the mountain site for both seasons, implying the uncertainties in emission inventories used in the CAMS reanalysis which may have more influence near source. Biases were reduced and the correlation coefficient of CAMS BC with observed BC increased when two datasets were compared on a daily basis, which suggests daily or longer time averaged CAMS BC could be more suitable for trend analysis. Although CAMS could generally reproduce the distinct diurnal variation of BC and CO on both sites, the inaccurate representation of the daily evolution of planetary boundary layer (PBL) in model may bring more uncertainties to the concentration biases on surface from midnight to early morning. BC hydrophilic ratio from CAMS displayed large biases compared to observations with no seasonal difference on both sites, which was probably resulted from the initial emission state of BC hygroscopicity for all source types in model. Uncertainties in the removal processes and the simplified aging processes in model could further induce uncertainty in modelling BC hydrophilic ratio in the CAMS. These results could not only be referenced for the improvement on CAMS reanalysis but also facilitate model or trend analysis of BC and CO pollution by utilizing the CAMS reanalysis product from both short- and long-term perspectives, which will be beneficial to both the mitigation and policy-making on primary emissions in China.