National Air Quality Monitoring Stations Research Articles

Long-term effects of air quality on hospital readmission for heart failure in patients with acute myocardial infarction

BackgroundCardiovascular disease (CVD) is the leading cause of death worldwide, with air pollution posing significant risks to cardiovascular health. The effect of air quality on heart failure (HF) readmission in acute myocardial infarction (AMI) patients is unclear.The aim of this study was to evaluate the role of a single measure of air pollution exposure collected on the day of first hospitalization. MethodsWe retrospectively analyzed data from 12,857 acute coronary syndrome (ACS) patients (January 2015–March 2023). After multiple screenings, 4023 AMI patients were included. The air pollution data is updated by the automatic monitoring data of the national urban air quality monitoring stations in real time and synchronized to the China Environmental Monitoring Station. Cox proportional hazards regression assessed the impact of air quality indicators on admission and outcomes in 4013 AMI patients. A decision tree model identified the most susceptible groups. ResultsAfter adjusting for confounders, NO2 (HR 1.009, 95% CI 1.004–1.015, P = 0.00066) and PM10 (HR 1.006, 95% CI 1.002–1.011, P = 0.00751) increased the risk of HF readmission in ST-segment elevation myocardial infarction (STEMI) patients. No significant effect was observed in non-STEMI (NSTEMI) patients (P > 0.05). STEMI patients had a 2.8-fold higher risk of HF readmission with NO2 > 13 μg/m3 (HR 2.857, 95% CI 1.439–5.670, P = 0.00269) and a 1.65-fold higher risk with PM10 > 55 μg/m3 (HR 1.654, 95% CI 1.124–2.434, P = 0.01064). ConclusionNO2 and PM10 are linked to increased HF readmission risk in STEMI patients, particularly when NO2 exceeds 13 μg/m3 and PM10 exceeds 55 μg/m3. Younger, less symptomatic male STEMI patients with fewer underlying conditions are more vulnerable to these pollutants.

ANALYSIS OF SPATIAL AND TEMPORAL VARIABILITY OF AEROSOL OPTICAL DEPTH OVER KARABUK USING MODIS

The concept of aerosol refers to the combination of microscopic solid or liquid particles present in the atmosphere along with a mixture of gases. These particles are suspended in the air at different sizes and are evaluated based on their ability to scatter or absorb light, which is quantified through a measurement known as aerosol optical depth. These particles' quantities are determined using specialized devices, commonly referred to as "aerosol optical depth meters" or "optical thickness meters." Additionally, through remote sensing technology, aerosol optical depth can also be measured via satellites. In this study, aerosol optical depth has been examined temporally and spatially in the Karabük province for 2022. For this aim, data from National Air Quality Monitoring Stations (NAQMS) situated nationwide was employed, along with MODIS satellite images. Data from five stations in Karabük province, namely Kardemir1, Kardemir2, Tören Alanı, 75.yıl, and Safranbolu, were used for temporal analysis, while satellite imagery was used for spatial analysis. The relationship between aerosol optical depths derived from MODIS satellite data using green and blue band information and station data was investigated. As a result, a 99% positive correlation was found between the two bands obtained from the MODIS satellite, and a significant correlation was observed between ground-based particulate matter 2.5(PM2.5) and particulate matter 10 (PM10) data. Data from the Tören Alanı station, which had a higher amount of data (357 days) compared to other stations, was used to determine this correlation. It was found that there was an 86.35% positive correlation among particulate matters. A moderate correlation was also identified between ground-based data and aerosol optical depth obtained from satellite imagery.

A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data

Since 2013, frequent haze pollution events in China have been attracting public attention, generating a demand to identify the haze areas using satellite observations. Many studies of haze recognition algorithms are based on observations from space-borne imagers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Himawari Imager (AHI). Since the haze pixels are frequently misidentified as clouds in the official cloud detection products, these algorithms mainly focus on recovering them from clouds. There are just a few studies that provide a more precise distinction between haze and clear pixels. The Medium Resolution Imaging Spectrometer II (MERSI-II), the imager aboard the FY-3D satellite, has similar bands to those of MODIS, hence, it appears to have equivalent application potential. This study proposes a novel MERSI haze mask (MHAM) algorithm to directly categorize haze pixels in addition to cloudy and clear ones. This algorithm is based on the fact that cloudy and clear pixels exhibit opposing visible channel reflectance and infrared channel brightness temperature characteristics, and clear pixels are relative brighter, and as well as this, there is a positive difference between their apparent reflectance values, at 0.865 μm and 1.64 μm, respectively, over bright surfaces. Compared with the Aqua/MODIS and MERSI-II official cloud detection products, these two datasets treat the dense aerosol loadings as certain clouds, possible clouds and possible clear pixels, and they treat distinguished light or moderate haze as possible clouds, possible clear pixels and certainly clear pixels, while the novel algorithm is capable of demonstrating the haze region’s boundary in a manner that is more substantially consistent with the true color image. Using the PM2.5 (particle matter with a diameter that is less than 2.5 μm) data monitored by the national air quality monitoring stations as the test source, the results indicated that when the ground-based PM2.5 ≥ 35 μg/cm3 is considered to be haze days, the samples with the recognition rate that is higher than 85% accounted for 72.22% of the total samples. When PM2.5 ≥ 50 μg/cm3 is considered as haze days, 83.33% of the samples had an identification rate that was higher than 85%. A cross-comparison with similar research methods showed that the method proposed in this study had better sensitivity to bright surface clear and haze areas. This study will provide a haze mask for subsequent quantitative inversion of aerosol characteristics, and it will further exert the application benefits of MERSI-II instrument aboard on FY3D satellite.

Associations of exposures to air pollution and greenness with mortality in a newly treated tuberculosis cohort.

Some previous studies had linked air pollutants and greenness to the risk of death from tuberculosis (TB). Only a few studies had examined the effect of particulate matter (PM2.5) on the mortality of TB, and few studies had assessed the impact and interaction of multiple air pollutants and greenness on the mortality of newly treated TB patients. The study included 29,519 newly treated TB patients from three cities in Anhui province. We collected meteorological data and five pollutants data from The National Meteorological Science Center and air quality monitoring stations. Greenness data were generated by remote sensing inversion of medium-resolution satellite images. We geocoded each patient based on the residential address to calculate the average exposure to air pollutants and the average greenness exposure for each patient during treatment. The Cox proportional risk regression model was used to evaluate the effects of air pollutants and greenness on mortality in newly treated tuberculosis patients. Our results found that the higher the concentration of air pollutants in the living environment of newly treated TB patients, the greater the risk of death: HR 1.135 (95% CI: 1.123-1.147) and HR 1.333 (95% CI: 1.296-1.370) per 10μg/m3 of PM2.5 and SO2, respectively. Greenness reduced the mortality among newly treated TB patients: HR for NDVI exposure 0.936 (95% CI: 0.925-0.947), HR for NDVI_250m exposure 0.927 (95% CI: 0.916-0.938), and HR for NDVI_500m exposure 0.919 (95% CI: 0.908-0.931). Stratifying the cohort by median greenness exposure, HRs for air pollutants were lower in the high greenness exposure group. Mortality in newly treated TB patients is influenced by air pollutants and greenness. Higher green exposure can mitigate the effects of air pollution. Improving air quality may help reduce mortality among newly treated TB patients.

Performance assessment of NOVA SDS011 low-cost PM sensor in various microenvironments

Over the last 10years, as a possible alternative to the conventional approach to air quality monitoring, real-time monitoring systems that use low-cost sensors and sensor platforms have been frequently applied. Generally, the long-term characteristics of low-cost PM sensors and monitoring have not been thoroughly documented except for a few widely used sensors and monitors. This article addresses the laboratory and field validation of three low-cost PM monitors of the same type that use the NOVA SDS011 PM sensor module over a 1-year period. In outdoor environments, we co-located low-cost PM monitors with GRIMM EDM180 monitors at the National Air Quality Monitoring stations. In indoor environments, we co-located them with a Turnkey Osiris PM monitor. Several performance aspects of the PM monitors were examined: operational data coverage, linearity of response, accuracy, precision, and inter-sensor variability. The obtained results show that inter-monitor R values were typically higher than 0.95 regardless of the environment. The tested monitors demonstrate high linearity in comparison with PM10 and PM2.5 concentrations measured in outdoor air with reference-equivalent instrumentation with R2 values ranging from 0.52 up to 0.83. In addition, very good agreement (R2 values ranging from 0.93 up to 0.97) with the gravimetric PM10 and PM2.5 method is obtained in the indoor environment (30 < RH < 70%). High RH (over 70%) negatively affected the PM monitors' response, especially in the case of PM10 concentrations (high overestimation).

Spatiotemporal Variations in Summertime Ground-Level Ozone around Gasoline Stations in Shenzhen between 2014 and 2020

Ground-level ozone has become the primary air pollutant in many urban areas of China. Oil vapor pollution from gasoline stations accelerates the generation of ground-level ozone, especially in densely populated urban areas with high demands for transportation. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) around gasoline stations is urgently needed. However, urban GOCs vary sharply over short distances, increasing the need for GOCs at a high-spatial resolution. Thus, a high-spatial resolution (i.e., 1 km) concentration retrieval model based on the GLM and BME method was developed to obtain the daily spatiotemporal characteristics of GOCs. The hourly ozone records provided by the national air quality monitoring stations and multiple geospatial datasets were used as input data. The model exhibited satisfactory performance (R2 = 0.75, RMSE = 10.86 µg/m3). The derived GOCs show that the ozone levels at gasoline stations and their adjacent areas (1~3 km away from the gasoline stations) were significantly higher than the citywide average level, and this phenomenon gradually eased with the increasing distance from the gasoline stations. The findings indicate that special attention should be given to the prevention and control of ground-level ozone exposure risks in human settlements and activity areas near gasoline stations.

Association between Ambient Air Pollutants and Pneumonia in Wuhan, China, 2014–2017

Objectives: To assess associations between short-time air pollution exposure and outpatient visits for pneumonia by the distributed lag nonlinear model (DLNM). Methods: Daily outpatient visits for pneumonia and air pollutant data were collected from Wuhan Basic Medical Insurance Database in China and 10 national air quality monitoring stations in Wuhan from 2014 to 2017, respectively. Taking the first percentile of the concentration as the reference, DLNM was used to estimate the impact of moderate (50th) and high levels (99th) of pollutants on pneumonia. Results: A total of 133,882 outpatient visits were identified during the period of the study. Moderate-level (P50) fine particulate matter (PM2.5) or sulfur dioxide (SO2) and high-level nitrogen dioxide (NO2) (P99) can increase the risk of pneumonia. The maximum RR was 1.198 (95% CI: 1.094–1.311) at lag0-11, 1.304 (95% CI: 1.166–1.458) at lag0-13, and 1.286 (95% CI: 1.060–1.561) at lag0-14, respectively. Females and children had greater risks. Conclusions: Short-time PM2.5, SO2, and NO2 exposure were associated with outpatient visits for pneumonia in Wuhan, China.

Inflammatory and oxidative stress responses of healthy elders to solar-assisted large-scale cleaning system (SALSCS) and changes in ambient air pollution: A quasi-interventional study in Xi'an, China

An outdoor solar assisted large-scale cleaning system (SALSCS) was constructed to mitigate the levels of fine particulate matter (PM2.5) in urban areas of Xi'an China, providing a quasi-experimental opportunity to examine the biologic responses to the changes in pollution level. We conducted this outdoor SALSCS based real-world quasi-interventional study to examine the associations of the SALSCS intervention and changes in air pollution levels with the biomarkers of systemic inflammation and oxidative stress in healthy elders. We measured the levels of 8-hydrox-2-deoxyguanosine (8-OHdG), Interlukin-6 (IL-6), as well as tumor necrosis factor alpha (TNF-α) from urine samples, and IL-6 from saliva samples of 123 healthy retired participants from interventional/control residential areas in two sampling campaigns. We collected daily 24-h PM2.5 samples in two residential areas during the study periods using mini-volume samplers. Data on PM10, gaseous pollutants and weather factors were collected from the nearest national air quality monitoring stations. We used linear mixed-effect models to examine the percent change in each biomarker associated with the SALSCS intervention and air pollution levels, after adjusting for time trend, seasonality, weather factors and personal characteristics. Results showed that the SALSCS intervention was significantly associated with decreases in the geometric mean of biomarkers by 47.6% (95% confidence interval: 16.5–67.2%) for 8-OHdG, 66% (31.0–83.3%) for TNF-α, 41.7% (0.2–65.9%) and 43.4% (13.6–62.9%) for urinary and salivary IL-6, respectively. An inter-quartile range increase of ambient PM2.5 exposure averaged on the day of the collection of bio-samples and the day before (34.1 μg/m3) was associated, albeit non-significantly so, with 22.8%–37.9% increases in the geometric mean of these biomarkers. This study demonstrated that the SALSCS intervention and decreased ambient air pollution exposure results in lower burden of systemic inflammation and oxidative stress in older adults.

Short-term impact of ambient temperature on the incidence of influenza in Wuhan, China.

Few studies have estimated the nonlinear association of ambient temperature with the risk of influenza. We therefore applied a time-series analysis to explore the short-term effect of ambient temperature on the incidence of influenza in Wuhan, China. Daily influenza cases were collected from Hubei Provincial Center for Disease Control and Prevention (Hubei CDC) from January 1, 2014, to December 31, 2017. The meteorological and daily pollutant data was obtained from the Hubei Meteorological Service Center and National Air Quality Monitoring Stations, respectively. We used a generalized additive model (GAM) coupled with the distributed lag nonlinear model (DLNM) to explore the exposure-lag-response relationship between the short-term risk of influenza and daily average ambient temperature. Analyses were also performed to assess the extreme cold and hot temperature effects. We observed that the ambient temperature was statistically significant, and the exposure-response curve is approximately S-shaped, with a peak observed at 23.57 ℃. The single-day lag curve showed that extreme hot and cold temperatures were both significantly associated with influenza. The extreme hot temperature has an acute effect on influenza, with the most significant effect observed at lag 0-1. The extreme cold temperature has a relatively smaller effect but lasts longer, with the effect exerted continuously during a lag of 2-4days. Our study found significant nonlinear and delayed associations between ambient temperature and the incidence of influenza. Our finding contributes to the establishment of an early warning system for airborne infectious diseases.

Short-term exposure to ambient air pollution and type 2 diabetes mortality: A population-based time series study

Acute health effects of air pollution on diabetes risk have not been fully studied in developing countries and the results remain inconsistent. This study aimed to investigate the association between short-term exposure to ambient air pollution and Type 2 diabetes mellitus (T2DM) mortality in China. Data on T2DM mortality from 2013 to 2019 were obtained from the Cause of Death Reporting System (CDRS) of Wuhan Center for Disease Control and Prevention. Air pollution data for the same period were collected from 10 national air quality monitoring stations of Wuhan Ecology and Environment Institute, including daily average PM2.5, PM10, SO2, and NO2. Meteorological data including daily average temperature and relative humidity were collected from Wuhan Meteorological Bureau. Generalized additive models (GAM) based on quasi-Poisson distribution were applied to evaluate the association between short-term exposure to air pollution and daily T2DM deaths. A total of 9837 T2DM deaths were recorded during the study period in Wuhan. We found that short-term exposure to PM2.5, PM10, SO2, and NO2 were positively associated with T2DM mortality, and gaseous pollutants appeared to have greater effects than particulate matter (PM). For the largest effect, per 10 μg/m3 increment in PM2.5 (lag 02), PM10 (lag 02), SO2 (lag 03), and NO2 (lag 02) were significantly associated with 1.099% (95% CI: 0.451, 1.747), 1.016% (95% CI: 0.517, 1.514), 3.835% (95% CI: 1.480, 6.189), and 1.587% (95% CI: 0.646, 2.528) increase of daily T2DM deaths, respectively. Stratified analysis showed that females or elderly population aged 65 and above were more susceptible to air pollution exposure. In conclusion, short-term exposure to air pollution was significantly associated with a higher risk of T2DM mortality. Further research is required to verify our findings and elucidate the underlying mechanisms.

Environmental effects of prohibiting urban fireworks and firecrackers in Jinan, China.

Eight national air quality monitoring stations were selected to examine the environmental effects of prohibiting fireworks and firecrackers since January 1, 2018, in Jinan, China, by using an air quality index (AQI) on three time scales. In 2014-2018, the average annual AQI decreased year on year, but a downward trend in 2018 was only found by applying a Daniel trend test. The change in monthly data for 2016-2018 followed a "W" pattern. The overall AQI value was lower on New Year's Eve than during Spring Festival, and the 2-day AQI in 2018 was lower than that in 2017. The GIS analysis method was used for spatial visualization. The AQI in the built-up part of Jinan was high in the west and low in the east on New Year's Eve and Spring Festival of 2017, being lowest in the Development Zone. The AQI spatial distribution was high in the city core but low in its periphery; in 2018, the high-AQI center appeared near the Provincial Seed Warehouse on New Year's Eve and Spring Festival. Multiple linear regression was used to analyze the relationship between AQI and pollutants. Six pollutants were found to be positively correlated with the AQI. PM2.5 and PM10 had the strongest correlations on New Year's Eve and Spring Festival, for which the correlations of SO2, CO, and NO2 were significantly weaker in 2018 than in 2017.

Impact of ambient particulate matter on respiratory-related school absence: a case-crossover study in China

School absence was a monitor of temporary and immediate change in the health status of children. Previous studies have focused on the relationship between short-term absent rates and air pollutants, ignoring the number of daily respiratory-related school absence which was a more obvious indicator of children health. To explore the association between the respiratory-related school absence and particulate matter, we implemented this research. The absence data was collected from the symptom monitoring system due to illness. Daily concentrations of pollutants were obtained from the national air quality monitoring stations in Qingdao, China, between January 1, 2014, and December 31, 2018. We used a case-crossover design and conditional logistic regression to investigate whether levels of particulate matter influence the respiratory-related school absence on school children in Qingdao, China. Results showed that the odds ratios (ORs) with 95% confidence intervals (CIs) of the respiratory-related school absence was 1.021 (1.019–1.024) and 1.015 (1.014–1.017) for each 10 μg/m3 increase in PM2.5 and PM10 at lag0 day. Subgroup analyses showed that low age groups between 6 and 12 years have a higher risk of absenting school than higher age groups aged 13–16 years. The risk of absenting school in the warm season was greater, while the gender difference was not significant. In summary, the short-term variation in PM2.5 and PM10 concentrations has a statistically significant positive association impact on respiratory-related school absences in Qingdao, China.

Prenatal exposure to air pollutants and childhood atopic dermatitis and allergic rhinitis adopting machine learning approaches: 14-year follow-up birth cohort study.

The incidence of childhood atopic dermatitis (AD) and allergic rhinitis (AR) is increasing. This warrants development of measures to predict and prevent these conditions. We aimed to investigate the predictive ability of a spectrum of data mining methods to predict childhood AD and AR using longitudinal birth cohort data. We conducted a 14-year follow-up of infants born to pregnant women who had undergone maternal examinations at nine selected maternity hospitals across Taiwan during 2000-2005. The subjects were interviewed using structured questionnaires to record data on basic demographics, socioeconomic status, lifestyle, medical history, and 24-h dietary recall. Hourly concentrations of air pollutants within 1year before childbirth were obtained from 76 national air quality monitoring stations in Taiwan. We utilized weighted K-nearest neighbour method (k=3) to infer the personalized air pollution exposure. Machine learning methods were performed on the heterogeneous attributes set to predict allergic diseases in children. A total of 1439 mother-infant pairs were recruited in machine learning analysis. The prevalence of AD and AR in children up to 14years of age were 6.8% and 15.9%, respectively. Overall, tree-based models achieved higher sensitivity and specificity than other methods, with areas under receiver operating characteristic curve of 83% for AD and 84% for AR, respectively. Our findings confirmed that prenatal air quality is an important factor affecting the predictive ability. Moreover, different air quality indices were better predicted, in combination than separately. Combining heterogeneous attributes including environmental exposures, demographic information, and allergens is the key to a better prediction of children allergies in the general population. Prenatal exposure to nitrogen dioxide (NO2) and its concatenation changes with time were significant predictors for AD and AR till adolescent.

Ambient air pollution and cerebrovascular disease mortality: an ecological time-series study based on 7-year death records in central China.

Most studies of short-term exposure to ambient air pollution and cerebrovascular diseases focused on specific stroke-related outcomes, and results were inconsistent due to data unavailability and limited sample size. It is unclear yet how ambient air pollution contributes to the total cardiovascular mortality in central China. Daily deaths from cerebrovascular diseases were obtained from the Disease Surveillance Point System (DSPs) of Wuhan Center for Disease Control and Prevention during the period from 2013 to 2019. Air pollution data were obtained from Wuhan Ecology and Environment Institute from 10 national air quality monitoring stations, including average daily PM2.5, PM10, SO2, NO2, and O3. Average daily temperature and relative humidity were obtained from Wuhan Meteorological Bureau. We performed a Poisson regression in generalized additive models (GAM) to examine the association between ambient air pollution and cerebrovascular disease mortality. We observed a total of 84,811 deaths from cerebrovascular diseases from 1 January 2013 to 31 December 2019 in Wuhan. Short-term exposure to PM2.5, PM10, SO2, and NO2 was positively associated with daily deaths from cerebrovascular diseases, and no significant association was found for O3. The largest effect on cerebrovascular disease mortality was found at lag0 for PM2.5 (ERR: 0.927, 95% CI: 0.749-1.105 per 10 μg/m3) and lag1 for PM10 (ERR: 0.627, 95% CI: 0.493-0.761 per 10 μg/m3), SO2 (ERR: 2.518, 95% CI: 1.914, 3.122 per 10 μg/m3), and NO2 (ERR: 1.090, 95% CI: 0.822-1.358 per 10 μg/m3). The trends across lags were statistically significant. The stratified analysis demonstrated that females were more susceptible to SO2 and NO2, while elder individuals aged above 65 years old, compared with younger people, suffered more from air pollution, especially from SO2. Short-term exposure to PM2.5, PM10, SO2, and NO2 were significantly associated with a higher risk of cerebrovascular disease mortality, and elder females seemed to suffer more from air pollution. Further research is required to reveal the underlying mechanisms.

Ambient air pollutants and hospital visits for pneumonia: a case-crossover study in Qingdao, China

BackgroundPneumonia is one of the principal reasons for incidence and death in the world. The former research mainly concentrated on specific sources of patients. Besides, due to the heterogeneity among regions, there are inconsistencies in the outcome of these surveys. To explore the relationship between atmospheric pollution and hospital visits for pneumonia under the climate and pollution conditions in Qingdao, we carried out this study.MethodsThe medical records of pneumonia patients were gathered from the affiliated hospital of Qingdao University during Jan 1st, 2014, and Dec 31st,2018. Daily concentrations of PM2.5, PM10, SO2, NO2, as well as CO, were collected from the national air quality monitoring stations in Qingdao. Case-crossover study design and conditional logistic regression model were used to estimate the associations. Daily temperature, relative humidity, and atmospheric pressure were adjusted as the covariates in all models. A principal component analysis was used to solve the multicollinearity between atmospheric pollutants and investigate the relationship between various air pollutants and pneumonia occurs.ResultsIn the single pollutant model, with interquartile range increment of the density of PM2.5, PM10, NO2 and SO2 at the lag2 days, the odds ratio of hospital visits for pneumonia patients increased by 6.4% (95%CI, 2.3–10.7%), 7.7% (95%CI, 3.2–12.4%), 6.7% (95%CI, 1.0–12.7%), and 7.2% (95%CI, 1.1–13.5%). Stratified analysis showed that pollutants were more significant in the cold period. Besides, the impact of atmospheric particulates on different ages mainly occurs in the young child (0 to 3-year-old). The odds ratio was 1.042 (95%CI, 1.012–1.072) when the principal components of atmospheric pollutants were included in the conditional logistic model.ConclusionsOur study found a significant relationship between short-term uncovering to PM2.5, PM10, NO2, SO2, and hospital visits for pneumonia in Qingdao. The effect of atmospheric pollutants mainly arose in a cold period. The particulate matter might be the principal reason in inducing hospital visits for pneumonia.

Evaluation of Variability in the Ambient PM2.5 Concentrations from FEM and FRM-like Measurements for Exposure Estimates

This study aims to evaluate the variability in ambient fine particulate matter (PM2.5) concentrations obtained from the federal equivalent method (FEM) and federal reference method (FRM)-like measurements at the national air quality monitoring stations (AQMSs) for exposure estimates and to examine the effect of environmental factors and sampling site characteristics affecting the spatial and temporal variations in PM2.5 concentrations. A mixed-effects model was used to evaluate the temporal and spatial variability in daily and annual PM2.5 concentrations during 2014-2017 at 16 AQMSs in the Big Taipei City, Taiwan. The mean FEM PM2.5 concentrations were ~30% higher than the FRM-like PM2.5 concentrations. The FRM-like PM2.5 concentrations obtained by applying the calibration procedures presented a negligible between-site variability. The daily FEM PM2.5 concentrations were dominated by the within-site variability (~90%), whereas the annual concentrations were reasonably attributable to the between-site variability (47.8%). Ambient PM2.5 was mainly affected by the gaseous pollutants (such as NO2, O3, and SO2), accounting for 45.8% and 26.8% of the within-site and between-site variability in concentrations, respectively. The FEM measurements rather than the FRM-like measurements at the AQMSs could provide a higher between-site variability for exposure estimates of PM2.5 in the epidemiological studies.

Effects of Rainfall on PM2.5 and PM10 in the Middle Reaches of the Yangtze River

Based on the PM2.5 and PM10 mass concentration data obtained from 51 national air quality monitoring stations and the corresponding rainfall intensity data in automatic meteorological stations in Hubei Province from 1 January 2015 to 31 December 2017, the impact of rainfall intensity on PM mass concentrations under relatively different humidity conditions was analyzed. The results showed that light rain occurred most frequently in the pollution process, with Xiangyang being affected for up to 587 h. PM concentration would not change drastically under the effect of precipitation. Mean rainfall intensity responsible for wet growth of PM10 and PM2.5 was mainly &lt;0.5 mm/h, while that responsible for wet removal of PM2.5 was significantly higher (&gt;1.4 mm/h) than that of PM10 (&gt;1.0 mm/h). Precipitation was more likely to produce a wet removal effect for a greater initial value of PM mass concentration, and on the contrary, a wet growth effect was more likely, with the threshold of PM10 mass concentration being 150 μg/m3 and that of PM2.5 mass concentration being 95 μg/m3. Wet removal played a leading role in lower humidity (∼60%) and greater rainfall intensity, but wet growth played a leading role in higher humidity (∼90%) and lower rainfall intensity. As the precipitation level increased (rainfall ≥1.5 mm·h−1), the wet removal to PM10 mass concentration was enhanced more obviously. The variations of PM2.5 had similar distributions to those of PM10 under the effect of precipitation, but the wet removal effect of precipitation was weakened and the wet growth effect was enhanced.

Acute effects of air pollution on the incidence of hand, foot, and mouth disease in Wuhan, China

Previous studies have shown that exposure to ambient air pollution is associated with hand, foot, and mouth disease (HFMD), but the results are limited. Our study aimed to investigate the effects of several major air pollutants on HFMD in the warm and cold seasons in central China. A Poisson Generalized additive model (GAM) was used to assess the association between short-term exposure to ambient air pollution and the incidence of HFMD at Wuhan, China during 20152016. Daily cases of HFMD were extracted from the infectious disease surveillance systems of Hubei provincial center for disease control and prevention (Hubei CDC). Data on air pollution, including sulfur dioxide (SO2), nitrogen dioxide (NO2), ground-level ozone (O3), particulate matter (PM) with an aerodynamic diameter less than or equal to 10 μm (PM10), and PM with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5) were obtained from 10 national air quality monitoring stations in Wuhan. We found that the association between air pollution and HFMD was most pronounced at lag days 10 and 11. The results of the single-pollutant model show that the associations of SO2, NO2, O3, PM10, and PM2.5 with HFMD were all statistically significant. The results of the multi-pollutant model showed that NO2, O3, PM10, and PM2.5 are reduced after excluding the confounding of co-pollutants. The seasonal analysis showed that the effect of air pollution in the cold season was higher than in the warm season. Our research provides more evidence related to air pollution and HFMD, and helps policy makers to develop regulations for the prevention and control of the disease.

Analysis of a Pollution Transmission Process in Hefei City Based on Mobile Lidar

The air pollution has been regional in China with the development of economy. To monitoring the air pollution transmission, a new technique, mobile lidar system (GBQ-S01), was introduced. In this paper, a pollution transmission process happened on October 26th, 2017, was analyzed with the use of mobile lidar, air quality monitoring stations data, and Hysplit backward trajectories. The results showed that the polluted air mass was transferred from northeast under the force of air pressure. Under the influences of air pollution transmission and bad meteorological diffusion conditions, The PM10 quality concentrations in Hefei increased a lot within 5 hours; among all the 10 national air quality monitoring stations, the Luyang District (the northernmost one) and Changjiang Middle Road (the easternmost one) received the most serious impact with PM10 concentration reached up to 252 μg/m3 and 219 μg/m3 at 22:00 (Beijing Time).

The improvement of spatial-temporal resolution of PM2.5 estimation based on micro-air quality sensors by using data fusion technique

With the rapid development of the Internet of things (IoTs) and modern industrial society, forecasting air pollution concentration, e.g., the concentration of PM2.5, is of great significance to protect human health and the environment. Accurate prediction of PM2.5 concentrations is limited by the number and the data quality of air quality monitoring stations. In Taiwan, the spatial and temporal data of PM2.5 concentrations are measured by 77 national air quality monitoring stations (built by Taiwan EPA). However, the national stations are costly and scarce because of the highly precise instrument and their size. Therefore, many places are still out of coverage of the monitoring network. Recently, under the framework of IoTs, there are hundreds of portable air quality sensors called “AirBox” developed jointly by the Taiwan local government and a private company. By virtue of its low price and portability, the AirBox can provide a higher resolution of space-time PM2.5 measurement. However, the spatiotemporal distribution is different between AirBox and EPA stations, and data quality and accuracy of AirBox is poorer than national air quality monitoring stations. Thus, to integrate the heterogeneous PM2.5 data, the data fusion technique should be used before further analysis.In this study, we propose a new data fusion method called multi-sensor space-time data fusion framework. It is based on the Optimum Linear Data Fusion theory and integrating with a multi-time step Kriging method for spatial-temporal estimation. The method is used to do heterogeneous data fusion from different sources and data qualities. It is able to improve the estimation of PM2.5 concentration in space and time. Results have shown that by combining PM2.5 concentration data from 1176 low-cost AirBoxes as additional information in our model, the estimation of spatial-temporal PM2.5 concentration becomes better and more reasonable. The r2 of the validation regression model is 0.89. Under the approach proposed in this study, we made the information of the micro-sensors more reliable and improved the higher spatial-temporal resolution of air quality monitoring. It could provide very useful information for better spatial-temporal data analysis and further environmental management, such as air pollution source localization, health risk assessment, and micro-scale air pollution analysis.