Annual Mean Concentrations Of PM2 Research Articles

European air quality in view of the WHO 2021 guideline levels: Effect of emission reductions on air pollution exposure

Although anthropogenic emissions have decreased during the last 2 decades, air pollution is still problematic in Europe. This study analyzes the air quality in Europe using simulations by EURopean Air pollution Dispersion—Inverse Model for the year 2016 with updated emissions in view of the annual guideline levels for particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) released in 2021 by the World Health Organization (WHO). Three different emission scenarios are applied, including a scenario for the committed emission reductions from the European Union (EU), a scenario including additional reductions to specifically mitigate PM2.5, and a scenario in which all anthropogenic emissions are eliminated. Model results show that in Europe, the concentrations of PM2.5 and NO2 exceed the annual WHO guideline levels by up to a factor of 5.6 and 5.2, respectively, in the main polluted regions and by up to a factor of 2 for O3 in Southern Europe. High concentrations of PM2.5 and O3 are homogeneously distributed across Europe with 99% and 100% of the European population exposed to concentrations above the WHO guideline levels, respectively. NO2 concentrations above the annual WHO guideline level are primarily found in populated areas, affecting 323 million inhabitants in 2016. Although the emission scenario designed to mitigate PM2.5 shows a decrease of the highest annual mean concentrations of PM2.5 from 28 µgm−3 to 12 µgm−3, 527 million European inhabitants remain affected by PM2.5 annual mean concentrations above the WHO guideline level. Seasonal mean O3 concentrations after eliminating all anthropogenic emissions (between 60 and 82 µgm−3) are found to be above the WHO guideline level for the entire European continent. The mortality attributable to air pollution is reduced by 47% in the emission scenario for committed emissions by the EU. In the more aggressive scenario designed to mitigate PM2.5, the mortality is reduced by 72%. The study reveals that the emission scenarios and, therefore, the reduction in premature deaths are subject to sectoral emission reductions between 41% and 79%.

Genetic susceptibility modifies the association of long-term air pollution exposure on Parkinson’s disease

Inconsistent findings exist regarding the potential association between polluted air and Parkinson’s disease (PD), with unclear insights into the role of inherited sensitivity. This study sought to explore the potential link between various air pollutants and PD risk, investigating whether genetic susceptibility modulates these associations. The population-based study involved 312,009 initially PD-free participants with complete genotyping data. Annual mean concentrations of PM2.5, PM10, NO2, and NOx were estimated, and a polygenic risk score (PRS) was computed to assess individual genetic risks for PD. Cox proportional risk models were employed to calculate hazard ratios (HR) and 95% confidence intervals (CI) for the associations between ambient air pollutants, genetic risk, and incident PD. Over a median 12.07-year follow-up, 2356 PD cases (0.76%) were observed. Compared to the lowest quartile of air pollution, the highest quartiles of NO2 and PM10 pollution showed HRs and 95% CIs of 1.247 (1.089–1.427) and 1.201 (1.052–1.373) for PD incidence, respectively. Each 10 μg/m3 increase in NO2 and PM10 yielded elevated HRs and 95% CIs for PD of 1.089 (1.026–1.155) and 1.363 (1.043–1.782), respectively. Individuals with significant genetic and PM10 exposure risks had the highest PD development risk (HR: 2.748, 95% CI: 2.145–3.520). Similarly, those with substantial genetic and NO2 exposure risks were over twice as likely to develop PD compared to minimal-risk counterparts (HR: 2.414, 95% CI: 1.912–3.048). Findings suggest that exposure to air contaminants heightens PD risk, particularly in individuals genetically predisposed to high susceptibility.

Mortality risk of people living with HIV under hypothetical intervention scenarios of PM2.5 and HIV severity: a prospective cohort study

This study estimated and compared mortality risks among people living with HIV (PLWH) under the real-world and hypothetical scenarios of PM2.5 concentrations and HIV severity. An open cohort from all PLWH receiving antiretroviral therapy in Sichuan during 2010–2019 was constructed, resulting in 541,515 person-years. Annual mean concentrations of PM2.5 were estimated and linked to PLWH by their residential address. The parametric g-formula were used to assess 3- and 5-year mortality risks under the real-world and hypothetical scenarios of PM2.5 (10–35, 35–50, 50–75 μg/m3) and CD4 concentrations (0–200, 200–500, 500–800, 800–1100 counts/μl). The estimated 3- and 5-year mortality risks among the PLWH were 14.43 % and 19.38 %, respectively, which would decrease substantially when annual PM2.5 concentration were reduced to between 10 and 35 μg/m3 (risk difference [RD] = −3.23 % and − 4.06 %) and would increase when PM2.5 concentration were elevated to between 50 and 75 μg/m3 (RD = 3.59 % and 5.04 %). The mortality risk would increase when CD4 concentration were reduced to <200 counts/μl (RD = 15.90 % and 20.27 %) and would decrease when CD4 concentration were ≥ 200 counts/μl, especially to between 800 and 1100 counts/μl (RD = -9.01 % and − 11.75 %). The elevated concentration of PM2.5 may disproportionately affect individuals with immune deficiency, especially those with more severity. The findings would serve as justifications for future intervention design and policy making to alleviate air pollution and improve environmental justice and health equity.

Particulate matter – a cancerous threat to our health?

Introduction: Particulate matter (PM) as a part of outdoor air pollutants are classified as human carcinogens. They are formed majorly as a result of combustion process by industry, power plants and engines. PM can be divided by the size of their particles into PM2.5 and PM10, where PM2.5 are small enough to penetrate into the alveoli sacs in the lung reaching the bloodstream, whereas PM10 affect mostly oral cavity, nose and the throat.&#x0D; Aim of the study: This study aims to investigate the impact of PM2.5 and PM10 particle concentrations in the EU NUTS 2 subregions on the death rates due to the most common malignant neoplasms.&#x0D; Results: There is a positive moderate correlation (r = 0.421; p &lt; 0.001) between the annual mean concentration of PM2.5 and deaths due to malignant tumors. The strongest correlation was observed for malignant neoplasms of larynx, which is a positive correlation with a strong effect (r = 0.641; p &lt; 0.001); and malignant neoplasm of bladder (r = 0.523; p &lt; 0.001). For PM10, there is a moderately weak positive correlation (r = 0,195; p = 0.008) between the annual average concentration of PM10 and deaths due to malignant tumors. The strongest correlation was observed for malignant neoplasms of larynx, which is a positive correlation with a strong effect (r = 0,551; p &lt; 0.001).&#x0D; Conclusion: The effect of PM impact on the malignant neoplasms is strong to moderate. The most affected neoplasm site are the ones the PMs intake occurs, being in the respiratory system. However other sites, where PMs can accumulate can be impacted as well. Further studies about the population with the highest risk due to the PMs exposure may be beneficial as other non-air quality-connected predictors may be found.

Chinese Provincial Air Pollutant Concentration Prediction over the Long Term

Although China’s urban air quality has improved, there are still many cities that do not meet China’s ambient air quality standards and experience serious air pollution problems, causing tremendous damage to people’s health and sustainable social development. For the sake of obtaining the specific time when China’s ambient air quality will reach the standard, the annual mean air pollutant concentrations of 27 Chinese provinces are predicted and analyzed. Based on original data from air pollutant concentrations in 27 Chinese provinces from 2017 to 2021, a gray prediction model with fractional order accumulation is established to analyze and predict the concentration of pollutants in 27 provinces. The applicability of the model is then validated by mean absolute percentage error values. According to the forecast results, by 2026, the concentrations of six pollutants, PM2.5, PM10, SO2, NO2, CO, and O3, will all meet Class II air quality standards in 25 Chinese provinces, namely Beijing, Chongqing, Shanghai, Hebei, Jiangsu, Shanxi, Zhejiang, Liaoning, Anhui, Jilin, Fujian, Heilongjiang, Jiangxi, Hubei, Qinghai, Hunan, Guangdong, Hainan, Guangxi, Guizhou, Shaanxi, Gansu, Yunnan, Inner Mongolia, and Xinjiang (corrected for the effect of sandstorms). Tianjin, Sichuan, and Xinjiang (not corrected for the effect of sandstorms) still exceed the standard in the annual mean concentration of PM2.5, NO2, and PM10, respectively. Sichuan and Tianjin are, respectively, expected to meet Class II air quality standards in 2027 and 2030, and Xinjiang (not corrected for the effect of sandstorms) is expected to fail to meet Class II standards in the next 15 years. Finally, the current situation with respect to China’s ambient air quality in 27 Chinese provinces is analyzed, and corresponding suggestions are put forward to offer an explicit direction for relevant departments.

Clean air in Europe for all: A call for more ambitious action.

Clean air in Europe for all: A call for more ambitious action.

Enhanced nitrate contribution to light extinction during haze pollution in Chengdu: Insights based on an improved multiple linear regression model

In recent years, the annual mean concentration of PM2.5 has decreased in Chengdu, China; however, atmospheric visibility has not improved accordingly. Low-visibility events occurred even when the PM2.5 mass concentrations were below the national ambient air quality secondary standard (daily mean concentration, 75 μg/m3). In this study, the non-linear relationship between PM2.5 and visibility was analyzed under different NO3− mass fractions in PM2.5 based on 2-year field observation data. The results indicated that NO3− formation contributed to particulate pollution events and reduced atmospheric visibility. Multiple linear regression was used to propose a localized reconstruction equation for the light-scattering coefficient. According to the maximum likelihood estimation method and log-transformed residuals, the mass scattering coefficients (MSEs) of organic matter (OM), NH4NO3, and (NH4)2SO4 in Chengdu were 7.42, 3.83, and 3.80, respectively. OM and NH4NO3 contributed to more than 50% of the light-extinction coefficient (bext). NH4NO3 was the main pollutant causing the substantial increase in bext. Chengdu has a high relative humidity (annual mean 70%), and under such conditions, the contribution of NH4NO3 to bext was considerably enhanced through hygroscopic growth and heterogeneous reactions. This study estimated the localized MSEs of OM, NH4NO3, and (NH4)2SO4 in Chengdu and emphasized that effective control measures to reduce nitrate and its precursors could simultaneously ameliorate air quality and visibility in humid regions with poor atmospheric visibility.

Characterization of Polycyclic Aromatic Hydrocarbons (PAHs) associated with fine aerosols in ambient atmosphere of high-altitude urban environment in Sikkim Himalaya

Polycyclic Aromatic Hydrocarbons (PAHs) compounds are ubiquitous in ambient air due to their persistence, carcinogenicity, and mutagenicity. Gangtok being one of the cleanest cities in India located in Eastern Himalayan region, witnesses high developmental activities with enhanced urbanization affecting the ambient air quality. The present study aims to measure PM2.5 and PAHs in the ambient atmosphere of the Sikkim Himalaya to understand the influence of natural and anthropogenic activities on aerosol loading and their chemical characteristics. The PM2.5 samples were collected and analysed for the duration from Jan 2020 to Feb 2021.The seasonal mean concentrations of PM2.5 and PAHs were observed to be high during autumn and low during summer season. Overall, the annual mean concentration of PM2.5 was found higher than the prescribed limit of World Health Organization and National Ambient Air Quality Standards. The concentration of the 16 individual PAHs were found to be highest during autumn season (55.26 ± 37.15 ng/m3). Among the different PAHs, the annual mean concentration of fluorene (3.29 ± 4.07 ng/m3) and naphthalene (1.15 ± 3.76 ng/m3) were found to be the highest and lowest, respectively. The Molecular Diagnostic Ratio (MDR) test reveals higher contribution from heavy traffic activities throughout the winter and autumn seasons. The other possible sources identified over the region are fossil fuel combustion, and biomass burning. The multivariate statistical analysis (Multifactor Principal Component Analysis) also indicates a strong association between PM2.5 /PAHs and meteorological variables across the region in different seasons. The precipitation and wind pattern during the study period suggests that major contribution of the PM2.5 and PAHs were from local sources, with minimal contribution from long-range transport. The findings are important for comprehending the trends of PAH accumulation over a high-altitude urban area, and for developing sustainable air quality control methods in the Himalayan region.

Are air quality perception and PM2.5 exposure differently associated with cardiovascular and respiratory disease mortality in Brussels? Findings from a census-based study

BackgroundThere is ample evidence that air pollution increases mortality risk, but most studies are based on modelled estimates of air pollution, while the subjective perception of air quality is scarcely assessed. We aimed to compare the effects of objective and subjective exposure to air pollution on cardiorespiratory mortality in Brussels, Belgium. MethodsData consisted of the 2001 Belgian census linked to registry-based mortality data for the follow-up period 2001–2014. We included individuals aged >30 years of age residing in Brussels at baseline (2001). Air pollution exposure was assessed with objective (modelled annual mean concentrations of PM2.5 in micrograms per cubic metre, μg/m3) and subjective indicators (poor self-reported air quality perception in the census). We used Cox Proportional Hazard models with age as the underlying time scale to evaluate associations with cardiovascular disease (CVD) and respiratory disease mortality, and separately, ischaemic heart disease (IHD), cerebrovascular disease, and COPD excluding asthma mortality. We specified single- and two-exposure models and evaluated effect modification by neighbourhood unemployment rate. Results437,340 individuals were included at baseline. During follow-up (2001–2014), 22,821 (5%) individuals had died from CVDs and 8572 (2%) from respiratory diseases. In single-exposure models, PM2.5 was significantly associated with an increased risk in CVD and IHD mortality (e.g. for IHD, per 5 μg/m3 increase: Hazard Ratio, HR:1.22, 95%CI:1.08–1.37), and poor air quality perception with COPD excluding asthma mortality (HR:1.23, 95%CI:1.15–1.33). Associations remained significant in the two-exposure models, and additionally, perception was associated with respiratory disease mortality. Associations became gradually stronger with increasing neighbourhood unemployment rate [e.g. in the highest, Q3: PM2.5 and cerebrovascular disease mortality (HR:1.53, 95%CI:1.04–2.24)]. ConclusionOur findings suggest that objective and subjective exposure to air pollution increased the risk of dying from cardiovascular and respiratory diseases respectively in Brussels. These results encourage policies reducing pollution load in Brussels whilst considering socio-economic inequalities.

Impact of lowering fine particulate matter from major emission sources on mortality in Canada: A nationwide causal analysis

Emissions of fine particulate matter (PM2.5) from human activities have been linked to substantial disease burdens, but evidence regarding how reducing PM2.5 at its sources would improve public health is sparse. We followed a population-based cohort of 2.7 million adults across Canada from 2007 through 2016. For each participant, we estimated annual mean concentrations of PM2.5 and the fractional contributions to PM2.5 from the five leading anthropogenic sources at their residential address using satellite observations in combination with a global atmospheric chemistry transport model. For each source, we estimated the causal effects of six hypothetical interventions on 10-y nonaccidental mortality risk using the parametric g-formula, a structural causal model. We conducted stratified analyses by age, sex, and income. This cohort would have experienced tangible health gains had contributions to PM2.5 from any of the five sources been reduced. Compared with no intervention, a 10% annual reduction in PM2.5 contributions from transportation and power generation, Canada's largest and fifth-largest anthropogenic sources, would have prevented approximately 175 (95%CI: 123-226) and 90 (95%CI: 63-117) deaths per million by 2016, respectively. A more intensive 50% reduction per year in PM2.5 contributions from the two sources would have averted 360 and 185 deaths per million, respectively, by 2016. The potential health benefits were greater among men, older adults, and low-income earners. In Canada, where PM2.5 levels are among the lowest worldwide, reducing PM2.5 contributions from anthropogenic sources by as little as 10% annually would yield meaningful health gains.

Risk-benefit analysis of the association of ambient fine particulate matter pollution and physical activity with age-related cataract: A prospective cohort study

The aim of this study was to examine the associations of fine particulate matter (PM2.5) and physical activity (PA) with age-related cataract and its subtypes, and to explore potential interactions between PM2.5 and PA. A total of 291,627 participants enrolled in the UK Biobank cohort during 2006–2010 was included. The annual mean concentration of PM2.5 at each participant's residential address was estimated using Land Use Regression models. PA was measured using the International Physical Activity Questionnaire. Cox proportional hazards model was used to examine the associations of PM2.5 and PA with incident age-related cataract, and to assess their interactions on additive and multiplicative scales. We identified 12,826 incident cases of age-related cataract, including 7686 cases of nuclear cataract and 5140 cases of non-nuclear cataract. Higher PM2.5 exposure was associated with a higher risk of age-related cataract [HR = 1.140; 95% CI: 1.105, 1.177; per 1 μg/m3 increase], and the association was stronger for non-nuclear cataract than for nuclear cataract. There was a negative association between PA and cataract [HR = 0.925; 95% CI: 0.879, 0.974; comparing high to low level]. No clear evidence of the interactions between PM2.5 and PA was found. Our findings indicated that high PM2.5 exposure and low levels of PA were independently associated with increased risks of incident age-related cataract.

Spatiotemporal characterization of aerosols and trace gases over the Yangtze River Delta region, China: impact of trans-boundary pollution and meteorology

BackgroundThe spatiotemporal variation of observed trace gases (NO2, SO2, O3) and particulate matter (PM2.5, PM10) were investigated over cities of Yangtze River Delta (YRD) region including Nanjing, Hefei, Shanghai and Hangzhou. Furthermore, the characteristics of different pollution episodes, i.e., haze events (visibility < 7 km, relative humidity < 80%, and PM2.5 > 40 µg/m3) and complex pollution episodes (PM2.5 > 35 µg/m3 and O3 > 160 µg/m3) were studied over the cities of the YRD region. The impact of China clean air action plan on concentration of aerosols and trace gases is examined. The impacts of trans-boundary pollution and different meteorological conditions were also examined.ResultsThe highest annual mean concentrations of PM2.5, PM10, NO2 and O3 were found for 2019 over all the cities. The annual mean concentrations of PM2.5, PM10, and NO2 showed continuous declines from 2019 to 2021 due to emission control measures and implementation of the Clean Air Action plan over all the cities of the YRD region. The annual mean O3 levels showed a decline in 2020 over all the cities of YRD region, which is unprecedented since the beginning of the China’s National environmental monitoring program since 2013. However, a slight increase in annual O3 was observed in 2021. The highest overall means of PM2.5, PM10, SO2, and NO2 were observed over Hefei, whereas the highest O3 levels were found in Nanjing. Despite the strict control measures, PM2.5 and PM10 concentrations exceeded the Grade-1 National Ambient Air Quality Standards (NAAQS) and WHO (World Health Organization) guidelines over all the cities of the YRD region. The number of haze days was higher in Hefei and Nanjing, whereas the complex pollution episodes or concurrent occurrence of O3 and PM2.5 pollution days were higher in Hangzhou and Shanghai.The in situ data for SO2 and NO2 showed strong correlation with Tropospheric Monitoring Instrument (TROPOMI) satellite data.ConclusionsDespite the observed reductions in primary pollutants concentrations, the secondary pollutants formation is still a concern for major metropolises. The increase in temperature and lower relative humidity favors the accumulation of O3, while low temperature, low wind speeds and lower relative humidity favor the accumulation of primary pollutants. This study depicts different air pollution problems for different cities inside a region. Therefore, there is a dire need to continuous monitoring and analysis of air quality parameters and design city-specific policies and action plans to effectively deal with the metropolitan pollution.

Stringent Emission Controls Are Needed to Reach Clean Air Targets for Cities in China under a Warming Climate.

Quantifying the threat that climate change poses to fine particle (PM2.5) pollution is hampered by large uncertainties in the relationship between PM2.5 and meteorology. To constrain the impact of climate change on PM2.5, statistical models are often employed in a different manner than physical-chemical models to reduce the requirement of input data. A majority of statistical models predict PM2.5 concentration (often log-transformed) as a simple function of meteorology, which could be biased due to the conversion of precursor gases to PM2.5. We reduced this bias by developing a unique statistic model where the sum of PM2.5 and the weighted precursor gases, rather than the PM2.5 alone, was predicted as a function of meteorology and a proxy of primary emissions, where the input data of PM10, CO, O3, NOx, and SO2 were obtained from routine measurements. This modification, without losing the simplicity of statistical models, reduced the mean-square error from 27 to 17% and increased the coefficient of determination from 47 to 67% in the model cross-validation using daily PM2.5 observations during 2013-2018 for 74 cities over China. We found a previously unrecognized mechanism that synoptic climate change in the past half-century might have increased low quantiles of PM2.5 more strenuously than the upper quantiles in large cities over China. Climate change during 1971-2018 was projected to increase the annual mean concentration of PM2.5 at a degree that could be comparable with the toughest-ever clean air policy during 2013-2018 had counteracted it, as inferred from the decline in the daily concentration of carbon monoxide as an inert gas. Our estimate of the impact of climate change on PM2.5 is higher than previous statistical models, suggesting that aerosol chemistry might play a more important role than previously thought in the interaction between climate change and air pollution. Our result indicated that air quality might degrade if the future synoptic climate change could continue interacting with aerosol chemistry as it had occurred in the past half-century.

Association of environmental and socioeconomic indicators with serious mental illness diagnoses identified from general practitioner practice data in England: A spatial Bayesian modelling study.

BackgroundThe evidence is sparse regarding the associations between serious mental illnesses (SMIs) prevalence and environmental factors in adulthood as well as the geographic distribution and variability of these associations. In this study, we evaluated the association between availability and proximity of green and blue space with SMI prevalence in England as a whole and in its major conurbations (Greater London, Birmingham, Liverpool and Manchester, Leeds, and Newcastle).Methods and findingsWe carried out a retrospective analysis of routinely collected adult population (≥18 years) data at General Practitioner Practice (GPP) level. We used data from the Quality and Outcomes Framework (QOF) on the prevalence of a diagnosis of SMI (schizophrenia, bipolar affective disorder and other psychoses, and other patients on lithium therapy) at the level of GPP over the financial year April 2014 to March 2018. The number of GPPs included ranged between 7,492 (April 2017 to March 2018) to 7,997 (April 2014 to March 2015) and the number of patients ranged from 56,413,719 (April 2014 to March 2015) to 58,270,354 (April 2017 to March 2018). Data at GPP level were converted to the geographic hierarchy unit Lower Layer Super Output Area (LSOA) level for analysis. LSOAs are a geographic unit for reporting small area statistics and have an average population of around 1,500 people. We employed a Bayesian spatial regression model to explore the association of SMI prevalence in England and its major conurbations (greater London, Birmingham, Liverpool and Manchester, Leeds, and Newcastle) with environmental characteristics (green and blue space, flood risk areas, and air and noise pollution) and socioeconomic characteristics (age, ethnicity, and index of multiple deprivation (IMD)). We incorporated spatial random effects in our modelling to account for variation at multiple scales.Across England, the environmental characteristics associated with higher SMI prevalence at LSOA level were distance to public green space with a lake (prevalence ratio [95% credible interval]): 1.002 [1.001 to 1.003]), annual mean concentration of PM2.5 (1.014 [1.01 to 1.019]), and closeness to roads with noise levels above 75 dB (0.993 [0.992 to 0.995]). Higher SMI prevalence was also associated with a higher percentage of people above 24 years old (1.002 [1.002 to 1.003]), a higher percentage of ethnic minorities (1.002 [1.001 to 1.002]), and more deprived areas.Mean SMI prevalence at LSOA level in major conurbations mirrored the national associations with a few exceptions. In Birmingham, higher average SMI prevalence at LSOA level was positively associated with proximity to an urban green space with a lake (0.992 [0.99 to 0.998]). In Liverpool and Manchester, lower SMI prevalence was positively associated with road traffic noise ≥75 dB (1.012 [1.003 to 1.022]). In Birmingham, Liverpool, and Manchester, there was a positive association of SMI prevalence with distance to flood zone 3 (land within flood zone 3 has ≥1% chance of flooding annually from rivers or ≥0.5% chance of flooding annually from the sea, when flood defences are ignored): Birmingham: 1.012 [1.000 to 1.023]; Liverpool and Manchester: 1.016 [1.006 to 1.026]. In contrast, in Leeds, there was a negative association between SMI prevalence and distance to flood zone 3 (0.959 [0.944 to 0.975]). A limitation of this study was because we used a cross-sectional approach, we are unable to make causal inferences about our findings or investigate the temporal relationship between outcome and risk factors. Another limitation was that individuals who are exclusively treated under specialist mental health care and not seen in primary care at all were not included in this analysis.ConclusionsOur study provides further evidence on the significance of socioeconomic associations in patterns of SMI but emphasises the additional importance of considering environmental characteristics alongside socioeconomic variables in understanding these patterns. In this study, we did not observe a significant association between green space and SMI prevalence, but we did identify an apparent association between green spaces with a lake and SMI prevalence. Deprivation, higher concentrations of air pollution, and higher proportion of ethnic minorities were associated with higher SMI prevalence, supporting a social-ecological approach to public health prevention. It also provides evidence of the significance of spatial analysis in revealing the importance of place and context in influencing area-based patterns of SMI.

Characterization, spatial distribution, and health risk assessment of polycyclic aromatic hydrocarbons and heavy metals bounded PM2.5 in urban air of Tabriz, Iran

&#x0D; &#x0D; &#x0D; &#x0D; Introduction: Air and its invisible components play an influential part in the existence of living beings. The two factors of urbanization and industrialization of cities increase the concentration of various compounds in the air. This study has been designed to examine seasonal characterization, spatial distribution and health risk assessment of sixteen Polycyclic Aromatic Hydrocarbons (PAHs) and eleven heavy metals bounded PM2.5 at 16 sites in urban air of Tabriz, Iran.&#x0D; Materials and methods: Glass-fiber filters, peripheral pumps and PMI holder were used with a total 3 L/min flow rate for 24 h sampling Particulate Matter less than 2.5 μm (PM2.5) every 4 seasons at 16 sites from 20nd February to 20th December. Proper solvents are consumed for extraction purposes of these Materials. Inductively Coupled Plasma-Optical Emission Spectrometry (ICP- OES) and Gas Chromatography–Mass Spectrometry GC/MS devices used for analyzing purposes and the spatial distribution of PAHs and heavy metals bounded PM2.5 investigated by ArcGIS 10.3. Excess Lifetime Cancer Risk (ELCR) values and carcinogenicity risk were also calculated for children and adults in different exposure pathways.&#x0D; Results: The annual mean concentrations of PM2.5 were 41.17 μg/m3, ∑16 PAHs bounded-PM2.5 were much higher in autumn and winter (217.47 and 178.32 ng/m3) compared to summer and spring (162.61 and 131.89 ng/m3). The annual mean concentrations of heavy metals bounded-PM2.5 were 138.69 ng/m3. Dermal carcinogenicity risk of exposure with PAHs and ELCR values of heavy metals indicated high risk for adults and children in some stations.&#x0D; Conclusion: Considering the topographic location of the region, Industrial areas and the frequent presence of temperature inversion, such comprehensive researches are needed for control policies&#x0D; &#x0D; &#x0D; &#x0D;

Long-term (2012–2020) PM10 concentrations and increasing trends in the Sistan Basin: The role of Levar wind and synoptic meteorology

The Sistan Basin in southeast Iran is one of the windiest and dustiest arid environments over the globe. The present study analyses, for the first time, long-term (April 2012 to March 2020) PM10 concentrations taken in Zabol (31.0324° N, 61.4902° E), the main city in Sistan, aiming to investigate the pollution trends, to assess the seasonality and the contribution of dust, to study the effect of wind and other meteorological parameters and to associate the extreme dust-related PM10 episodes with synoptic meteorological conditions. The annual-mean PM10 concentration was 429 μg m−3, which is one of the highest over the globe, while in summer, PM10 increased up to 693 μg m−3, with most turbid months July and August. The mean PM10 concentrations were much higher than the median ones, driven by severe dust storms throughout the year (193 days with PM10 > 1000 μg m−3), but with higher frequency and intensity in summer. A statistically-significant increasing trend was found in PM10 concentrations from 2012 to 2020, associated with a pronounced increase in wind speed, especially after 2017. PM10 concentrations were significantly correlated with mean and maximum wind speeds (r = 0.46, 0.41), while the visibility was highly reduced by increasing wind speed and PM10 levels. Dust contributes most (∼64%) to AOD and consequently to PM10, while the highest (>90th percentile) PM10 concentrations, both in summer and winter, were linked to intensification of the Caspian Sea high-pressure system that modulates the wind regime and meteorological patterns in central and southwest Asia.

Assessment of Ambient Particulate Matter Concentrations- PM10, PM2.5 and PM1 at Rinchending in Phuentsholing, Bhutan

This paper presents the analysis and interpretation of daily, monthly and seasonal cycle of 1- minute average Particulate Matter (PM10, PM2.5 and PM1) concentrations measured in Rinchending, located on the hilltop, 4 km away from Phuentsholing, the second largest city in Bhutan between the period of March 2018 to March 2019. In diurnal cycle, the highest concentrations of PM were observed between 9AM – 10 AM in the morning and 5PM-7PM in the evening corresponding to the peak traffic hours. The concentrations of PM showed highest in the post-monsoon season, corresponding to October to February in Bhutan (PM10=57.36 μg/m3, PM2.5=33.73 μg/m3 and, PM1=29.28) compared to monsoon season corresponding to June-September (PM10=22.70 μg/m3, PM2.5=15.51 μg/m3 and, PM1=11.35) and pre-monsoon season corresponding to March-May (PM10=54.9 μg/m3, PM2.5=30.58 μg/m3 and, PM1=24.36). The frequency distribution of PM10 showed that upto 25% of the time, the concentration was upto 20.8 μg/m3, 50% of the time, the concentration was upto 35.3 μg/m3 and 75% of the time, the concentration was upto 59 μg/m3.Similarly, the frequency distribution of PM2.5 showed that upto 25% of the time, the concentration was upto 13.5 μg/m3, 50% of the time, the concentration was upto 22 μg/m3 and 75% of the time, the concentration was upto 38 μg/m3.The annual mean concentrations of PM10 (45.08 μg/m3) were violating the Annual WHO ambient air quality standard (20 μg/m3). The annual mean concentrations of PM2.5 (26.83μg/m3) were also violating the Annual WHO ambient air quality standard (10 μg/m3). The wind analysis done through the wind rose diagram found a dominant south and south-west wind pattern.

Urban Fine Particulate Matter and Elements Associated with Subclinical Atherosclerosis in Adolescents and Young Adults.

The relationships between the elemental constituents of PM2.5 and atherosclerosis remain limited, especially in young populations. This study included 755 subjects aged 12-30 years in the Taipei metropolis. A land use regression model was used to estimate residential annual mean concentrations of PM2.5 and eight elemental constituents. We evaluated the percent differences in carotid intima-media thickness (CIMT) with PM2.5 and elemental constituent exposures by linear regressions. Interquartile range increments for PM2.5 (4.5 μg/m3), sulfur (108.6 ng/m3), manganese (2.0 ng/m3), iron (34.5 ng/m3), copper (3.6 ng/m3), and zinc (20.7 ng/m3) were found to associate with 0.92% (95% confidence interval (CI): 0.17-1.66), 0.51% (0.02-1.00), 0.36% (0.05-0.67), 0.98% (0.15-1.82), 0.74% (0.01-1.48), and 1.20% (0.33-2.08) higher CIMTs, respectively. Factor analysis identified four air pollution source-related factors, and the factors interpreted as traffic and industry sources were associated with higher CIMTs. Stratified analyses showed the estimates were more evident in subjects who were ≥18 years old, females, or who had lower household income. Our study results provide new insight into the impacts of source-specific air pollution, and future research on source-specific air pollution effects in young populations, especially in vulnerable subpopulations, is warranted.

Influence of urban air pollution on the population in the Klang Valley, Malaysia: a spatial approach

BackgroundUrban air pollution resulting from economic growth as well as urbanization impacts the health of inhabitants. This study aims to examine the spatial distribution of particulate matter (PM10) and the risk of exposure for the population in the Klang Valley, an urban conglomeration centred in Kuala Lumpur, Malaysia.MethodsThe inverse distance weighted (IDW) interpolation technique in the ArcGIS platform was used to map the air quality from five air monitoring stations. Spatial statistics of the Global Moran’s I Spatial Autocorrelation and Optimized Hot Spot Analysis were used to estimate the spatial clustering of PM10. The population-weighted exposure level (PWEL) technique was used to calculate the population density susceptible to PM10 and the AirQ+ model was applied to estimate the proportion of the adult population at risk of chronic bronchitis due to long-term exposure to PM10 concentrations in the Klang Valley.ResultsThe highest annual mean PM10 concentrations at the Klang station ranged between 80 and 100 μg/m3 from 2000 to 2009 and exceeded the New Malaysia Ambient Air Quality Standard (NMAAQS) and the World Health Organization Air Quality Guidelines (WHOAQG) levels of 40 and 20 μg/m3, respectively. The Moran's I results indicated that the statistically significant clusters of high PM10 in Klang present a health risk to the local community. The PWEL results showed that levels of PM10 in Petaling Jaya exceeded the WHOAQG limits, potentially posing a health risk to the high-density population. The AirQ+ model found Klang to be a significantly high-risk area for chronic bronchitis among the adult population with 804,240 people (with a relative risk of 1.85) in 2009.ConclusionThese high-risk values are due to rapid urbanization and high motor vehicle usage in the Klang Valley and therefore the impact of PM10 on the population needs to be reduced. This situation should be taken seriously by the Department of Environment and Kuala Lumpur City Hall in addressing air pollution issues through stricter rules on emissions and implementing low-carbon policies to improve the well-being of residents.

Downscaling of air pollutants in Europe using uEMEP_v6

Abstract. The air quality downscaling model uEMEP and its combination with the EMEP MSC-W chemical transport model are used here to achieve high-resolution air quality modelling at street level in Europe. By using publicly available proxy data, this uEMEP–EMEP modelling system is applied to calculate annual mean NO2, PM2.5, PM10, and O3 concentrations for all of Europe down to 100 m resolution and is validated against all available AIRBASE monitoring stations in Europe at 25 m resolution. Downscaling is carried out on annual mean concentrations, requiring special attention to non-linear processes, such as NO2 chemistry for which frequency distributions are applied to better represent the non-linear NO2 chemistry. The downscaling shows significant improvement in NO2 concentrations for which the spatial correlation has been doubled for most countries and bias reduced from −46 % to −18 % for all stations in Europe. The downscaling of PM2.5 and PM10 does not show improvement in spatial correlation but does reduce the overall bias in the European calculations from −21 % to −11 % and from −39 % to −30 % for PM2.5 and PM10, respectively. There is improved spatial correlation in most countries after downscaling of O3 and a reduced positive bias of O3 concentrations from +16 % to +11 %. Sensitivity tests in Norway show that improvements in the emission and emission proxy data used for the downscaling can significantly improve both the NO2 and PM results. The downscaling development opens the way for improved exposure estimates, improved assessment of emissions, and detailed calculations of source contributions to exceedances in a consistent way for all of Europe at high resolution.