Mean PM10 Levels Research Articles

Assessing the impact of the National Clean Air Programme in Uttar Pradesh's non-attainment cities: a prophet model time series analysis

BackgroundUttar Pradesh, India’s largest state, faces critical pollution levels, necessitating urgent action. The National Clean Air Programme (NCAP) targets a 40% reduction in particulate pollution by 2026. This study assesses the NCAP’s impact on 15 non-attainment cities in Uttar Pradesh using the Prophet forecasting model. MethodsMonthly data from 2016 to 2023 on AQI and PM10 concentrations were sourced from the Uttar Pradesh Pollution Control Board. Significant changes in mean AQI and PM10 levels from 2017 to 2023 were evaluated using the Friedman Test. Prophet models forecasted PM10 concentrations for 2025-26, with relative percentage changes calculated and model evaluation metrics assessed. FindingsMost cities exhibited unhealthy air quality. Jhansi had the lowest AQI (72·73) in 2023, classified as 'moderate' by WHO standards. Gorakhpur consistently showed 'poor' AQI levels, peaking at 249·31 in 2019. Western Uttar Pradesh cities such as Ghaziabad, Noida, and Moradabad had significant pollution burdens. Predictions showed Bareilly with over a 70% reduction in PM10 levels, Raebareli 58%, Moradabad 55%, Ghaziabad 48%, Agra around 41%, and Varanasi 40%, meeting NCAP targets. However, Gorakhpur and Prayagraj predicted increases in PM10 levels by 50% and 32%, respectively. Moradabad’s model showed the best performance with an R2 of 0·81, MAE of 17·27 μg/m3, and MAPE of 0·10. InterpretationForecasting PM10 concentrations in Uttar Pradesh's non-attainment cities offers policymakers substantial evidence to enhance current efforts. While existing measures are in place, our findings suggest that intensified provisions may be necessary for cities predicted to fall short of meeting program targets. The Prophet model's forecasts can pinpoint these at-risk areas, allowing for targeted interventions and regional adjustments to strategies. This approach will help promote sustainable development customized to each city's specific needs. FundingNo funding was issued for this research.

Visualising Daily PM10 Pollution in an Open-Cut Mining Valley of New South Wales, Australia—Part II: Classification of Synoptic Circulation Types and Local Meteorological Patterns and Their Relation to Elevated Air Pollution in Spring and Summer

Abstract: The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (air-borne particles with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The present study was initiated to quantitatively examine the spatial and temporal variability of PM10 pollution in the region. An earlier paper of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns were quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology–air pollution relationships were visualised and interpreted on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs. the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs. elevated PM10 pollution in the valley. Two local meteorological patterns, the hot–dry–northwesterly wind conditions and the hot–dry–calm conditions, were found to be the most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of an eastward migrating continental high-pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere.

Characterizing indoor environmental quality in Portuguese office buildings for designing an intervention program

Intervention studies have been explored to identify actions to effectively remediate indoor environmental quality (IEQ) problems and to improve people's health, well-being, comfort, and productivity. This study assessed a comprehensive set of IEQ indicators related to ventilation, air pollution, thermal comfort, illuminance, and noise for the first time in Portuguese office buildings. The purpose was to derive evidence-based corrective measures for a further environmental intervention program. The study monitored and surveyed 15 open-space offices from six modern office buildings in Porto (Portugal) during a workday between September and December 2022. Illuminance was of most concern among the assessed IEQ indicators since the measured levels were below the minimum limit required in 27% of the evaluated workplaces. For CO2, although mean concentrations were below 1000 ppm, absolute values exceeding that level were consistently registered in 20% of the offices during the afternoon period. Mean levels of PM2.5, PM10, and ultrafine particles exceeding the WHO guidelines were found in 13%, 7%, and 7% of the offices, respectively. The assessed thermal comfort levels were typically neutral, corresponding to an estimated mean of 6% of dissatisfied people. Based on the findings, an intervention plan was designed to be implemented in the further stages of this work. The priority interventions to test include relocation of printers (PM source removal), optimisation of ventilation rates (using real-time data from CO2 sensors), adjustment of desk positions to improve illuminance, and introduction of indoor plants.

Urban PM2.5 concentrations in a small Colombian city and the impact associated with particle emissions generated by small-scale lime production

The stricter guidelines for fine particles PM2.5 recently published by the World Health Organization also motivate smaller cities to assess the exposure levels. In this study, PM2.5 was assessed in the municipality of Vijes, an important lime production center in the Cauca River Valley, Colombia. The main objective was to determine PM2.5 concentration levels in the urban background of the city and to estimate the contribution from industrial sources located west of the urbanized area. The assessment of PM2.5 concentrations in a city without fixed air quality monitors, meteorological stations, and information on emission sources, was designed to be expedient and possible to perform with a very restricted budget. Four low-cost optical sensors and one low-cost meteorological station were installed during two separate campaigns, each three to four months long. The PM2.5 measurements were analyzed with the support of meteorological data and dispersion modeling. Mean levels of PM2.5 in the urban background were found to be below the Colombian limit value of 25 µg m–3, in the range of 14 to 19 µg m–3, and with lower levels in the city center. The monitor located in the westernmost urban area, closest to the industrial plants, registered a high 24-h mean level close to the national limit value. The industrial contribution to long-term PM2.5 concentrations in the urban background of Vijes was estimated to be within a maximum of 6 µg m–3, i.e., a minor fraction of the monitored PM2.5 mean levels in the urban background. The dominating part of the PM2.5 concentrations could be attributed to other anthropogenic sources within or east of Vijes, as well as originating from the regional background concentration characterizing the Cauca River Valley to the east of Vijes, where pre-harvest sugar cane burning is common.

Particulate matter exposure from motorized traffic and risk of conversion from mild cognitive impairment to dementia: An Italian prospective cohort study

BackgroundBased on epidemiologic and laboratory studies, exposure to air pollutants has been linked to many adverse health effects including a higher risk of dementia. In this study, we aimed to evaluate the effect of long-term exposure to outdoor air pollution on risk of conversion to dementia in a cohort of subjects with mild cognitive impairment (MCI). MethodsWe recruited 53 Italian subjects newly-diagnosed with MCI. Within a geographical information system, we assessed recent outdoor air pollutant exposure, by modeling air levels of particulate matter with equivalent aerodynamic diameter ≤10 μm (PM10) from motorized traffic at participants’ residence. We investigated the relation of PM10 concentrations to subsequent conversion from MCI to any type of dementia. Using a Cox-proportional hazards model combined with a restricted cubic spline model, we computed the hazard ratio (HR) of dementia with its 95% confidence interval (CI) according to increasing PM10 exposure, adjusting for sex, age, and educational attainment. ResultsDuring a median follow up of 47.3 months, 34 participants developed dementia, in 26 cases diagnosed as Alzheimer's dementia. In non-linear restricted spline regression analysis, mean and maximum annual PM10 levels positively correlated with cerebrospinal fluid total and phosphorylated tau proteins concentrations, while they were inversely associated with β-amyloid. Concerning the risk of dementia, we found a positive association starting from above 10 μg/m3 for mean PM10 levels and above 35 μg/m3 for maximum PM10 levels. Specific estimates for Alzheimer's dementia were substantially similar. Adding other potential confounders to the multivariable model or removing early cases of dementia onset during the follow-up had little effect on the estimates. ConclusionsOur findings suggest that exposure to outdoor air pollutants, PM10 in particular, may non-linearly increase conversion from MCI to dementia above a certain ambient air concentration.

Ambient Air Pollution and Stillbirths Risk in Sydney, Australia.

We aimed to determine the associations between ambient air pollution, specifically particulate matter less than or equal to 10 microns and 2.5 microns (PM10 and PM2.5 respectively) and ozone (O3), and stillbirths. We analysed all singleton births between 20–42 weeks gestation in metropolitan Sydney, Australia, from 1997 to 2012. We implemented logistic regression to assess the associations between air pollutants and stillbirth for each trimester and for the entire pregnancy. Over the study period, there were 967,694 live births and 4287 stillbirths. Mean levels of PM10, PM2.5 and O3 for the entire pregnancy were 17.9 µg/m3, 7.1 µg/m3 and 3.2 ppb, respectively. Adjusted odds ratios were generally greater than unity for associations between PM and stillbirths, but none were statistically significant. There were no significant associations between O3 and stillbirths. There was potential effect modification of the PM10 and O3 association by maternal age. We did not find consistent evidence of associations between PM and O3 and stillbirths in Sydney, Australia. More high quality birth cohort studies are required to clarify associations between air pollution and stillbirths.

Long-term exposure to low concentrations of air pollution and cause-specific mortality beyond cardiorespiratory disease: A Danish nationwide cohort study

BACKGROUND AND AIM: The association between air pollution and mortality from cardiorespiratory disease is well established, yet evidence for other causes is limited. We examined the associations of long-term exposure to air pollution with mortality from lower respiratory infection (LRI), diabetes, dementia, Alzheimer’s disease (AD), and psychiatric disease (PD) in a Danish nationwide cohort study. METHODS: We linked 3,083,227 subjects aged ≥30 years in 2000 to the Danish Cause of Death Registry until 2017. Annual mean concentrations of fine particulate matter (PM₂.₅), nitrogen dioxide (NO₂), and black carbon (BC) in 2010 at 100x100m resolution were estimated with European-wide hybrid land-use regression models developed within ELAPSE (Effects of Low-Level Air Pollution: A Study in Europe). We applied Cox proportional hazard models with age as time scale, parish as cluster, and adjusting for sex, country of origin, income, education, employment status, marital status, and parish and region-level indicators of income and employment. RESULTS:During the follow-up, 805,838 subject died: 30,578 from LRI, 20,735 from diabetes, 41,178 from dementia, 11,894 from AD, and 12,801 from PD. Mean levels of PM₂.₅, NO₂ and BC were 12.4 µg/m³, 20.3 µg/m³ and 1.0 10⁻⁵/m, respectively. Hazard ratios (95% confidence intervals) for associations of PM₂.₅, NO₂, and BC with LRI mortality were 1.16 (1.10-1.21) per 5 µg/m³, 1.07 (1.04-1.09) per 10 µg/m³, and 1.05 (1.03-1.08) per 0.5 10⁻⁵/m, respectively. Corresponding estimates for diabetes mortality were 1.11 (1.04-1.17), 1.03 (1.00-1.05), and 1.00 (0.98-1.03); for dementia mortality were 1.05 (1.00-1.10), 1.05 (1.03-1.07), and 1.03 (1.01-1.05); for AD mortality were 1.24 (1.14-1.36), 1.18 (1.13-1.22), and 1.15 (1.11-1.19); and for PD mortality were 1.39 (1.28-1.51), 1.24 (1.20-1.28), and 1.22 (1.18-1.27). CONCLUSIONS:Long-term exposures to PM₂.₅, NO₂, and BC were associated with mortality other than cardiorespiratory diseases, even stronger for mortality from Alzheimer’s and psychiatric disease. The indirect adjustment for missing risk factors will be applied. KEYWORDS: Air pollution, Big data, Long-term exposure, Mortality, Neurodegenerative outcomes

Effects of air pollution and climatology on COVID-19 mortality in Spain.

The health, economic, and social impact of COVID-19 has been significant across the world. Our objective was to evaluate the association between air pollution (through NO2 and PM2.5 levels) and COVID-19 mortality in Spanish provinces from February 3, 2020, to July 14, 2020, adjusting for climatic parameters. An observational and ecological study was conducted with information extracted from Datadista repository (Datadista, 2020). Air pollutants (NO2 and PM2.5 levels) were analyzed as potential determinants of COVID-19 mortality. Multilevel Poisson regression models were used to analyze the risk of mortality after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Models were adjusted by four climatic variables (hours of solar radiation, precipitation, daily temperature and wind speed) and population size. The mean levels of PM2.5 and NO2 across all provinces and time in Spain were 8.7 μg/m3 (SD 9.7) and 8.7 μg/m3 (SD 6.2), respectively. High levels of PM2.5 (IRR = 1.016, 95% CI: 1.007–1.026), NO2 (IRR = 1.066, 95% CI: 1.058–1.075) and precipitation (IRRNO2 = 0.989, 95% CI: 0.981–0.997) were positively associated with COVID-19 mortality, whereas temperature (IRRPM2.5 = 0.988, 95% CI: 0.976–1.000; and IRRNO2 = 0.771, 95% CI: 0.761–0.782, respectively) and wind speed (IRRNO2 = 1.095, 95% CI: 1.061–1.131) were negatively associated with COVID-19 mortality. Air pollution can be a key factor to understand the mortality rate for COVID-19 in Spain. Furthermore, climatic variables could be influencing COVID-19 progression. Thus, air pollution and climatology ought to be taken into consideration in order to control the pandemic.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11869-021-01062-2.

Assessment of the Dynamic Exposure to PM2.5 Based on Hourly Cell Phone Location and Land Use Regression Model in Beijing.

The spatiotemporal locations of large populations are difficult to clearly characterize using traditional exposure assessment, mainly due to their complicated daily intraurban activities. This study aimed to extract hourly locations for the total population of Beijing based on cell phone data and assess their dynamic exposure to ambient PM2.5. The locations of residents were located by the cellular base stations that were keeping in contact with their cell phones. The diurnal activity pattern of the total population was investigated through the dynamic spatial distribution of all of the cell phones. The outdoor PM2.5 concentration was predicted in detail using a land use regression (LUR) model. The hourly PM2.5 map was overlapped with the hourly distribution of people for dynamic PM2.5 exposure estimation. For the mobile-derived total population, the mean level of PM2.5 exposure was 89.5 μg/m3 during the period from 2013 to 2015, which was higher than that reported for the census population (87.9 μg/m3). The hourly activity pattern showed that more than 10% of the total population commuted into the center of Beijing (e.g., the 5th ring road) during the daytime. On average, the PM2.5 concentration at workplaces was generally higher than in residential areas. The dynamic PM2.5 exposure pattern also varied with seasons. This study exhibited the strengths of mobile location in deriving the daily spatiotemporal activity patterns of the population in a megacity. This technology would refine future exposure assessment, including either small group cohort studies or city-level large population assessments.

Geospatial analysis of individual-based Parkinson's disease data supports a link with air pollution: A case-control study

BackgroundThe etiology of Parkinson's disease (PD) remains unknown. To approach the issue of PD's risk factors from a new perspective, we hypothesized that coupling the geographic distribution of PD with spatial statistics may provide new insights into environmental epidemiology research. The aim of this case-control study was to examine the spatial dependence of PD prevalence in the Canton of Geneva, Switzerland (population = 474,211). MethodsPD cases were identified through Geneva University Hospitals, private neurologists and nursing homes medical records (n = 1115). Controls derived from a population-based study (n = 12,614) and a comprehensive population census dataset (n = 237,771). All individuals were geographically localized based on their place of residence. Spatial Getis-Ord Gi* statistics were used to identify clusters of high versus low disease prevalence. Confounder-adjustment was performed for age, sex, nationality and income. Tukey's honestly significant difference was used to determine whether nitrogen dioxide and particulate matters PM10 concentrations were different within PD hotspots, coldspots or neutral areas. ResultsConfounder-adjustment greatly reduced greatly the spatial association. Characteristics of the geographic space influenced PD prevalence in 6% of patients. PD hotspots were concentrated in the urban centre. There was a significant difference in mean annual nitrogen dioxide and PM10 levels (+3.6 μg/m3 [p < 0.001] and +0.63 μg/m3 [p < 0.001] respectively) between PD hotspots and coldspots. ConclusionPD prevalence exhibited a spatial dependence for a small but significant proportion of patients. A positive association was detected between PD clusters and air pollution. Our data emphasize the multifactorial nature of PD and support a link between PD and air pollution.

Estimation of pregnancy losses attributable to exposure to ambient fine particles in south Asia: an epidemiological case-control study

Maternal exposure to ambient particulate matter (PM2·5) is associated with pregnancy loss (ie, stillbirth and miscarriage). South Asia has the highest burden of pregnancy loss globally and is one of the most PM2·5 polluted regions in the world. However, knowledge of the relevant exposure-response function for mothers is insufficient. In this epidemiological case-control study, we collected data from Demographic and Health Surveys from India, Pakistan, and Bangladesh for the period 1998-2016 for women who reported at least one pregnancy loss and one or more livebirths. We assessed ambient exposure during gestation with satellite-based PM2·5 measurements for the period. To derive the exposure-response function, we did a self-compared case-control study in which each case of pregnancy loss was compared with a successful livebirth control or controls by the same mother. Using the estimated exposure-response function, we quantified pregnancy losses attributable to PM2·5 in the region for the period 2000-16 using a standard risk assessment approach. We assessed data for 34 197 mothers, who had at least one pregnancy loss and one or more livebirth, of whom 26 282 (76·9%) were from India, 4228 (12·4%) were from Pakistan, and 3687 (10·8%) were from Bangladesh. The 34 197 cases of pregnancy loss were matched with 76 282 livebirth controls. The mean level of PM2·5 exposure was 56·00 μg/m3 (SD 30·82) for cases and 54·57 μg/m3 (31·73) for controls. After adjustment for maternal age, non-linear terms for temperature and humidity, seasonal variation, and long-term trends, each 10 μg/m3 increment in PM2·5 was associated with an odds ratio for pregnancy loss of 1·03 (95% CI 1·02-1·05). According to the estimated non-linear exposure-response function by age and urban residence versus rural residence, for the period 2000-16, an estimated 349 681 (95% CI 152 932-489 493) pregnancy losses per year were attributed to ambient air exposure of more than 40 μg/m3 (the annual PM2·5 standard of India), accounting for 7·1% (95% CI 3·3-31·2) of the total annual pregnancy loss burden in south Asia for this period. However, our estimates could be biased because of the limitations of the data (eg, misclassification of induced and spontaneous pregnancy losses). Our findings add to epidemiological evidence of the association between pregnancy loss and PM2·5. Suboptimal air quality contributes to a considerable fraction of total pregnancy loss in south Asia. Controlling PM2·5 pollution will promote maternal health in south Asia. Chinese Natural Science Foundation and Ministry of Science and Technology of China.

Air Pollution Measurements and Land-Use Regression in Urban Sub-Saharan Africa Using Low-Cost Sensors—Possibilities and Pitfalls

Air pollution is recognized as the most important environmental factor that adversely affects human and societal wellbeing. Due to rapid urbanization, air pollution levels are increasing in the Sub-Saharan region, but there is a shortage of air pollution monitoring. Hence, exposure data to use as a base for exposure modelling and health effect assessments is also lacking. In this study, low-cost sensors were used to assess PM2.5 (particulate matter) levels in the city of Adama, Ethiopia. The measurements were conducted during two separate 1-week periods. The measurements were used to develop a land-use regression (LUR) model. The developed LUR model explained 33.4% of the variance in the concentrations of PM2.5. Two predictor variables were included in the final model, of which both were related to emissions from traffic sources. Some concern regarding influential observations remained in the final model. Long-term PM2.5 and wind direction data were obtained from the city’s meteorological station, which should be used to validate the representativeness of our sensor measurements. The PM2.5 long-term data were however not reliable. Means of obtaining good reference data combined with longer sensor measurements would be a good way forward to develop a stronger LUR model which, together with improved knowledge, can be applied towards improving the quality of health. A health impact assessment, based on the mean level of PM2.5 (23 µg/m3), presented the attributable burden of disease and showed the importance of addressing causes of these high ambient levels in the area.

Associations between ventilation, indoor air quality, and health in residential buildings

Background: While there is an increasing interest in zero-energy buildings, limited evidence is available on how building characteristics affect the level of indoor air quality and occupants’ health status. This study aimed to compare the indoor air quality between the zero-energy building and general apartment in Nowon-gu, Seoul, South Korea and to investigate the relationship between occupants’ behavior, indoor air quality, and level of symptoms.Methods: We recruited twenty-five households for case (zero-energy house) and control group (general apartment) respectively. Self-reported questionnaires were conducted on demographic profiles, general housing conditions, occupants’ behaviors and disease history at the baseline. The severity of symptoms including headache, cough, and allergic symptoms, and subjective stress level were asked using a Likert scale on a weekly basis through a web survey tool. Exposure variables including particulate matter (PM), Carbon dioxide (CO2), and Volatile organic compounds (VOCs) were measured using a real-time monitor (Airguard K) on every minute. Regression analysis and a generalized additive model (GAM) will be used to estimate the relative risk for each subgroup.Results: Case and control group showed difference in building age, residence period (23.2 vs. 48.3 months, p &lt; 0.001) and the housing size (55.0 vs. 81.8m2, p &lt; 0.001). Nearly half (48% vs. 40%) of occupants responded they ventilate the house every day. A majority of occupants (64%) used kitchen ventilator while cooking. Hourly mean levels of PM10, PM2.5, CO2 and VOCs between November to February were significantly low in zero-energy building (p &lt;0.001). Frequency of self-reported symptoms were not significantly different between two groups.Conclusions: The results of this study would contribute to finding physical housing conditions and occupants’ behaviors that are associated with occupants’ health and quality of life. It will also provide guidelines for the management of indoor air quality at home.

Low-level air pollution and cause-specific mortality in Denmark: the ELAPSE project

BackgroundThe association between air pollution and mortality requires further investigation in areas with low air pollution levels for evaluation of current air quality limits. Within the Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) study we examined associations between long-term exposure to air pollution and cause-specific mortality in a Danish national administrative cohort.MethodsWe linked 3,334,143 Danish adults aged ≥30 years in 2000 to the Danish Cause of Death Registry until 2015. Annual mean concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2) and black carbon (BC) were centrally modelled with European-wide hybrid ELAPSE land-use regression model in 2010 at 100m2 resolution. We applied Cox proportional hazard models for natural and cause-specific mortality in association with annual mean air pollution with age as underlying time, and adjusting for sex, education, employment status, birth country, area-level income and unemployment, and area-level lung cancer and COPD mortality rates.ResultsDuring 45,932,793 person-years of follow-up, we observed 817,029 deaths from natural causes in total, of which 245,489, 89,909, and 67,389 deaths were due to cardiovascular disease (CVD), respiratory disease (RD), and cerebrovascular disease (CeVD), respectively. Mean levels of PM2.5, NO2 and BC were 12.4 µg/m3, 20.2 µg/m3 and 1,0 10-5m-1, respectively. Hazard ratios and 95% confidence intervals for associations of PM2.5, NO2 and BC with natural mortality were 1.13 (1.11-1.16) per 5 µg/m3, 1.09 (1.08-1.10) per 10 µg/m3, and 1.08 (1.07-1.10) per 0.5 10-5m-1, respectively. Corresponding estimates for CVD mortality were 1.11 (1.08-1.15), 1.07 (1.06-1.09) and 1.07 (1.05-1.08); for RD mortality were 1.13 (1.09-1.18), 1.12 (1.10-1.15) and 1.10 (1.08-1.13); and for CeVD mortality were 1.07 (1.02-1.11), 1.04 (1.02-1.07) and 1.04 (1.02-1.06). Associations persisted below PM2.5 and NO2 EU limit values for all outcomes.ConclusionsLong-term exposures to PM2.5, NO2 and BC were associated with mortality even below EU limit values.

Long-term Exposure to Low Concentration of PM2.5 and Mortality: A Danish Nurse Cohort Study

Background/Aim. The association between air pollution and mortality is well established, yet studies in areas with low levels of air pollution, below EU limit values, with adjustment for road traffic noise, are sparse. We examined the association of long-term exposure to fine particulate matter (PM2.5) with mortality due to cardiovascular disease (CVD), respiratory disease (RD), and diabetes as well as all-cause natural mortality.Methods. We used data on 24,541 female nurses from the Danish Nurse Cohort who, at recruitment in 1993 or 1999, reported information on risk factors. Data on death were obtained from the Danish Register of Causes of Death until the end of 2013. Annual mean concentrations of PM2.5 in 1990-2013 at the nurses’ residences were estimated using the Danish air pollution dispersion modeling system. We examined associations between the 3-year running mean of PM2.5 with all-cause natural and cause-specific mortality by using time-varying Cox Regression models, adjusting for individual characteristics.Results. During the study time-period, 3,708 nurses died, and of these, 843 were due to CVD, 310 due to RD, and 64 due to diabetes. The mean level of PM2.5 at the baseline was 20.5 µg/m3. In the fully adjusted models, including road traffic noise, we found positive associations of 3-year running mean of PM2.5 with all-cause natural (hazard ratio; 95% confidence interval: 1.06; 1.01-1.11), CVD (1.14; 1.03-1.26), and diabetes mortality (1.41; 1.05-1.90), per interquartile range of 4.39 μg/m3. In subjects exposed to low levels of PM2.5 (&lt; 20 µg/m3), we found even stronger association with all-cause natural (1.19; 1.11-1.27), CVD (1.27; 1.01-1.46), RD (1.27; 1.00-1.60), and diabetes mortality (1.44; 0.83-2.48).Conclusion. In a Danish cohort of female nurses, long-term exposures to low-levels of PM2.5 were associated with all-cause mortality, strongest for diabetes, and CVD, which persisted at the levels below the current EU limit values.

Long-term exposure to air pollution and atherosclerosis in the carotid arteries in the Malmö diet and cancer cohort

BackgroundLong-term exposure to air pollution increases the risk of cardiovascular morbidity and mortality, but the mechanisms are not fully known. Current evidence suggests that air pollution exposure contributes to the development of atherosclerosis. There are few studies investigating associations between air pollution and carotid plaques, a well-known precursor of cardiovascular disease. MethodsA Swedish population-based cohort (aged 45–64 years at recruitment) was randomly selected from the Malmö Diet and Cancer study between 1991 and 1994, of which 6103 participants underwent ultrasound examination of the right carotid artery to determine carotid plaque presence and carotid intima media thickness (CIMT). Participants were assigned individual residential air pollution exposure (source-specific PM2.5, PM10, NOx, BC) at recruitment from Gaussian dispersion models. Logistic and linear regression models, adjusted for potential confounders and cardiovascular risk factors, were used to investigate associations between air pollutants and prevalence of carotid plaques, and CIMT, respectively. ResultsThe prevalence of carotid plaques was 35%. The mean levels of PM2.5 and PM10 at recruitment were 11 and 14 μg/m3, most of which was due to long range transport. The exposure contrast within the cohort was relatively low. PM2.5 exposure was associated with carotid plaques in a model including age and sex only (OR 1.10 (95% CI 1.01–1.20) per 1 μg/m3), but after adjustment for cardiovascular risk factors and socioeconomic status (SES) the association was weak and not significant (OR 1.05 (95% CI 0.96–1.16) per 1 μg/m3). The pattern was similar for PM10 and NOx exposure. Associations between air pollutants and plaques were slightly stronger for long-term residents and in younger participants with hypertension. There was no clear linear trend between air pollution exposure and plaque prevalence. Non-significant slightly positive associations were seen between air pollution exposures and CIMT. ConclusionsIn this large, well-controlled cross-sectional study at low exposure levels we found no significant associations between air pollution exposures and subclinical atherosclerosis in the carotid arteries, after adjusting for cardiovascular risk factors and SES. Further epidemiological studies of air pollution and intermediate outcomes are needed to explain the link between air pollution and cardiovascular events.

Changes in air pollution levels after COVID-19 outbreak in Korea

In order to control the spread of COVID-19, social distancing measures were implemented in many countries. This study investigated changes in air pollution during the social distancing after the COVID-19 outbreak in Korea. Ambient PM2.5, PM10, NO2, and CO that are particularly related to industrial activities and traffic were reduced during the social distancing in response to the COVID-19 outbreak. In March 2020, immediately after social distancing, mean levels of PM2.5, PM10, NO2, and CO decreased nationwide from last year's mean levels by 16.98 μg/m3, 21.61 μg/m3, 4.16 ppb, and 0.09 ppm, respectively (p-value for the year-to-year difference <0.001, =0.001, =0.008, <0.001), a decrease by 45.45%, 35.56%, 20.41%, and 17.33%, respectively. Changes in ambient O3 or SO2 were not observed to be attributable to social distancing. Our findings, that such effort for a short period of time resulted in a significant reduction in air pollution, may point toward reducing air pollution as a public health problem in a more sustainable post-COVID-19 world.

Temporal fluctuations of PM2.5 and PM10, population exposure, and their health impacts in Dezful city, Iran.

Morbidity and mortality impacts of particulate matter (PM) are globally important health critical parameters. In this ecological-descriptive study, the health impact of PM10 and PM2.5 associated with there temporal variations in Dezful city were assessed from 2013 to 2015. AirQ+ software handles the PM air pollutants by addressing impact evaluation and life table evaluation. We used a new method to analysis fine particles feature by using regular daily observations of PM10. In this method, relationship between PM2.5 and PM10 mass concentrations were analyzed and calculated. The annual average concentrations of PM10 were 147.1, 114.3 and 158.8μg/m3, and the annual average concentration of PM2.5 were 57.8, 50.7 and 58.2μg/m3 in 2013, 2014 and 2015, respectively. PM10 also had obvious diurnal variations with highest hourly concentrations in 13:00 and 22:00 but the lowest concentrations often occurred in 05:00 and 16:00. Unexpectedly, in weekends the concentration of PM pollutants appeared to have increased from 18:00 to midnight. The daily based analysis showed that there are 147 dusty days in the study period during which the most severe dusty day occurred in 2014. Over the study period, mean levels of PM10 and PM2.5 in both conditions were higher in 2015 compare to 2013 and 2014, which probably is due to higher frequency of dust storms in 2015. Hence, during 2015 and 2013 they're were higher morbidity and mortality compare to 2014 due to exposure to higher polluted air with PMs in all cases except lung cancer (LC).

Out-Door Air Pollution Levels in Vehicular-Traffic Junctions in Nsukka Metropolis, Enugu Metropolis and Awgu Semi-Urban Area in Enugu State, Nigeria

Studies were carried out to investigate the outdoor air pollution levels in vehicular traffic junctions in the major cities of Nsukka, Enugu and semi-urban area of Awgu all in Enugu State, Nigeria using standard analytical procedures. PM2.5 was collected using Envirotech air sampler, APM 550 and analyzed gravimetrically. Other determined air pollutant gases such as SO2, NO2, O3 and CO were analyzed using colorimetric techniques. The mean hourly traffic density in the vehicular traffic junctions in Nsukka metropolis, Enugu metropolis and Awgu were 2015, 2873 and 587 respectively. The mean range of values of PM2.5, NO2, SO2, O3 and CO in vehicular traffic junctions within the investigated environments were 1.67 - 12.16 μg/m3, 3.72 - 23.83 μg/m3, 2.96 - 30.09 μg/m3, 5.45 - 66.54 μg/m3 and 1.18 - 15.17 ppm respectively. The mean levels of the determined air pollutants in the air around vehicular traffic junctions in Nsukka metropolis, Enugu metropolis and Awgu semi-urban area differed significantly. The mean levels of PM2.5, and CO in the air around vehicular traffic junctions in Enugu metropolis and CO in the air around traffic junctions in Nsukka metropolis were above the recommended permissible limits. Traffic density was therefore seen as the single most important factor contributing to the varying air pollution levels observed in the investigated environments.

Statistical analysis of atmospheric pollutant concentrations in parts of Imo State, Southeastern Nigeria

This study was carried out to analyze the concentrations of some atmospheric pollutants in some parts of Imo State, southeastern Nigeria using statistical techniques. In the present study, PM10, NO2, SO2, CO, VOC and H2S in some parts of Imo State Southeastern Nigeria were analyzed to determine their presence, concentrations and possible sources of emission. This study also tried to establish possible relationship between pollutants spatial variation and dispersion with industrial and anthropogenic activities within the study area. The study area includes Egbema, Orlu, Okigwe and Orlu areas of Imo State, Nigeria. Air quality data was collected three times a day (morning, afternoon and evening) using the Gasman Air Monitors which was specifically used for SO2, NO2 and CO measurements, the Haze-dust Particulate Monitor 10 µm was used for PM10 data acquisition. Similarly, the Aeroqual Gas Monitor Model 300 was used for H2S measurement while the Ibrid MX6 Gas Monitor was used for VOC measurement. Results of the study revealed that the mean PM10 and CO levels in the locations exceeded the 24 h limit of 150 µg/m3 and 35 ppm of the United States National Ambient Air Quality Standards (US NAAQS) respectively. In addition, the mean concentrations of NO2 was above the annual limit of 0.053 ppm as prescribed by US NAAAQS, while the mean values of SO2 were within the annual and 1–hour limits of 0.5 ppm and 0.075 ppm respectively as stipulated by the US NAAQS. Whereas significant concentrations of H2S were not detected in the various locations, elevated concentration of VOC were recorded within Egbema area indicating possible influence from gas flaring and other crude oil related activities. The ANOVA and Box and Whisker plots revealed remarkable variations of the air pollutant concentrations in the study area. In addition, while the Hierarchical Cluster Analysis (HCA) grouped the pollutants into one major group, Principal Component Analysis (PCA) further grouped them into one coherent component indicating similar source of emission. The findings of this study therefore showed the need for regular air quality monitoring of the area to forestall adverse environmental problems.