Study of Trends in Concentrations of Basic Air Pollutants in the Malopolska Province

Abstract The purpose of the paper was to analyse the trends observed at air monitoring stations in the Malopolska Province - one of the most polluted regions in Poland. The study was carried out on the basis of long-term measurement data registered at five selected stations of automatic monitoring of air quality in the Malopolska Province. Trends evaluation was made on the basis of mean annual concentrations, taken from the database of the Chief Inspectorate for Environmental Protection in Poland. Separately for each basic air pollutant, such as SO2, NO2, NOx, CO, PM10 and O3, trend lines and their linear equations were determined to illustrate the direction of changes in concentrations. The obtained equations of the trend lines indicate the threat to the environment in the Malopolska Province. Based on the results obtained it can be concluded that for recent years there has been observed the concentration decrease of main air pollutants, except of tropospheric ozone.

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Association between long-term exposure to ambient air pollutants and mortality rates because of circulatory and respiratory diseases in South Korea

10.21203/rs.2.23984/v1 ◽

2020 ◽

Author(s):

Jinhee Kwon ◽

Jeongeun Hwang ◽

Hahn Yi ◽

Hyun-Jin Bae ◽

Miso Jang ◽

...

Keyword(s):

Air Pollution ◽

Heart Disease ◽

Air Quality ◽

Air Pollutants ◽

Respiratory Diseases ◽

Mortality Rates ◽

Quality Standard ◽

Air Pollutant ◽

Pm 10

Abstract Background : Associations between long-term exposure to common air pollutants including nitrogen dioxide, carbon monoxide, sulfur dioxide (SO 2 ), ozone, and particulate matter (PM 10 ) and health consequences have been studied. We investigated spatial effects of exposure to air pollution on mortality by circulatory and respiratory diseases nation-wide and in metropolitan. Methods: Means of daily concentration of the common air pollutants from 2005 to 2016 were calculated by district unit using linear interpolation. Age-standardized mortality rates of people suffering from heart disease (HD); cerebrovascular disease (CVD); ischemic heart disease (IHD); pneumonia (PN) and chronic lower respiratory disease (CLRD) were acquired from population census data. Sub-divided comparisons were performed to adjust spatial heterogeneity. Pearson’s correlation coefficients between mortality rates and air pollutant concentrations were investigated. Multivariable linear regressions were performed to investigate associations considering confounding factors. Results: Air pollutant concentration in metropolitan was the highest, except SO 2 ; in particular, PM 10 concentration was higher than air quality standard (PM 10 : 55.27 µg/m 3 , air quality standard: 50.00 µg/m 3 , P<0.05). Pearson’s correlation coefficient between PM 10 and mortality rates was significant ( r =0.313, 0.596, 0.420, -0.277 and 0.523 for HD, CVD, IHD, PN, and CLRD, all P<0.05) in metropolitan. The powers of regression model for PM 10 , smoking rate, education level, and population density were 0.532 and 0.482 (adjusted R 2 ) for mortality rates of CVD and CLRD, respectively. Conclusion : Long-term exposure study with sub-divided analysis showed overall associations between air pollution exposure and circulatory and respiratory disease mortalities. PM 10 exposure was significantly associated with mortality of CVD and CLRD in metropolitan.

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Application of combined model of stepwise regression analysis and artificial neural network in data calibration of miniature air quality detector

Scientific Reports ◽

10.1038/s41598-021-82871-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Bing Liu ◽

Qingbo Zhao ◽

Yueqiang Jin ◽

Jiayu Shen ◽

Chaoyang Li

Keyword(s):

Neural Network ◽

Air Quality ◽

Regression Model ◽

Multilayer Perceptron ◽

Stepwise Regression ◽

Control Point ◽

Measurement Data ◽

Environmental Indicators ◽

Air Pollutant ◽

Mlp Model

AbstractIn this paper, six types of air pollutant concentrations are taken as the research object, and the data monitored by the micro air quality detector are calibrated by the national control point measurement data. We use correlation analysis to find out the main factors affecting air quality, and then build a stepwise regression model for six types of pollutants based on 8 months of data. Taking the stepwise regression fitting value and the data monitored by the miniature air quality detector as input variables, combined with the multilayer perceptron neural network, the SRA-MLP model was obtained to correct the pollutant data. We compared the stepwise regression model, the standard multilayer perceptron neural network and the SRA-MLP model by three indicators. Whether it is root mean square error, average absolute error or average relative error, SRA-MLP model is the best model. Using the SRA-MLP model to correct the data can increase the accuracy of the self-built point data by 42.5% to 86.5%. The SRA-MLP model has excellent prediction effects on both the training set and the test set, indicating that it has good generalization ability. This model plays a positive role in scientific arrangement and promotion of miniature air quality detectors. It can be applied not only to air quality monitoring, but also to the monitoring of other environmental indicators.

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Evaluation of the Policy Effect of China’s Environmental Interview System for Effective Air Quality Governance

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18179006 ◽

2021 ◽

Vol 18 (17) ◽

pp. 9006

Author(s):

Xue Jin ◽

Ussif Rashid Sumaila ◽

Kedong Yin ◽

Zhichao Qi

Keyword(s):

Air Quality ◽

Environmental Protection ◽

Environmental Sustainability ◽

Local Governments ◽

Local Governance ◽

Assessment System ◽

Control Group ◽

Short Term ◽

Interview System

The Ministry of Ecology and Environment of the People’s Republic of China formally proposed an environmental interview system in May 2014, which applies pressure on local governments to fulfill their responsibility toward environmental protection by conducting face-to-face public interviews with their officials. In this paper, 48 cities that were publicly interviewed from 2014–2020 were considered the experimental group and 48 cities surrounding them were the control group. First, the dynamic panel model is applied to initially determine the effect of the policy. Then, a regression discontinuity method (Sharp RD) is used to analyze the short-term and long-term effects and compare the reasons for the differences observed among the estimates of various types of samples. Finally, a series of robustness tests were also conducted. The results show that the environmental interview system can improve air quality. However, because an emergency short-term local governance system exists at present, the governance effect is not long-term and, therefore, not sustainable. Therefore, it suggests that the government should continue to improve the environmental interview system, establish an optimal environmental protection incentive mechanism, and encourage local governments to implement environmental protection policies effectively in the long term. The results of the research are of great significance to the environmental impact assessment system of the world, especially in countries with similar economic systems, which are facing a trade-off between economic growth and environmental sustainability.

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Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: Impacts of COVID-19 pandemic lockdown

10.5194/acp-2020-1169 ◽

2020 ◽

Author(s):

Shibao Wang ◽

Yun Ma ◽

Zhongrui Wang ◽

Lei Wang ◽

Xuguang Chi ◽

...

Keyword(s):

Air Quality ◽

Air Pollutants ◽

Low Cost ◽

Air Pollutant ◽

Mitigation Strategies ◽

Traffic Emissions ◽

Urban Air ◽

Industrial Emissions ◽

Gis Technology ◽

Mobile Monitoring

Abstract. The development of low-cost sensors and novel calibration algorithms provides new hints to complement conventional ground-based observation sites to evaluate the spatial and temporal distribution of pollutants on hyper-local scales (tens of meters). Here we use sensors deployed on a taxi fleet to explore the air quality in the road network of Nanjing over the course of a year (Oct. 2019–Sep. 2020). Based on GIS technology, we develop a grid analysis method to obtain 50 m resolution maps of major air pollutants (CO, NO2, and O3). Through hotspots identification analysis, we find three main sources of air pollutants including traffic, industrial emissions, and cooking fumes. We find that CO and NO2 concentrations show a pattern: highways > arterial roads > secondary roads > branch roads > residential streets, reflecting traffic volume. While the O3 concentrations in these five road types are in opposite order due to the titration effect of NOx. Combined the mobile measurements and the stationary station data, we diagnose that the contribution of traffic-related emissions to CO and NO2 are 42.6 % and 26.3 %, respectively. Compared to the pre-COVID period, the concentrations of CO and NO2 during COVID-lockdown period decreased for 44.9 % and 47.1 %, respectively, and the contribution of traffic-related emissions to them both decreased by more than 50 %. With the end of the COVID-lockdown period, traffic emissions and air pollutant concentrations rebounded substantially, indicating that traffic emissions have a crucial impact on the variation of air pollutants levels in urban regions. This research demonstrates the sense power of mobile monitoring for urban air pollution, which provides detailed information for source attribution, accurate traceability, and potential mitigation strategies at urban micro-scale.

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Stand-alone low-cost sensor network in the inner city of Munich for modeling urban air pollutants

10.5194/egusphere-egu21-15182 ◽

2021 ◽

Author(s):

Adrian Wenzel ◽

Jia Chen ◽

Florian Dietrich ◽

Sebastian T. Thekkekara ◽

Daniel Zollitsch ◽

...

Keyword(s):

Air Quality ◽

High Resolution ◽

Sensor Network ◽

Air Pollutants ◽

Low Cost ◽

Air Pollutant ◽

Sensor Nodes ◽

Sensor Data ◽

Small Scale ◽

Urban Air

Modeling urban air pollutants is a challenging task not only due to the complicated, small-scale topography but also due to the complex chemical processes within the chemical regime of a city. Nitrogen oxides (NOx), particulate matter (PM) and other tracer gases, e.g. formaldehyde, hold information about which chemical regime is present in a city. As we are going to test and apply chemical models for urban pollution &#8211; especially with respect to spatial and temporally variability &#8211; measurement data with high spatial and temporal resolution are critical.Since governmental monitoring stations of air pollutants such as PM, NOx, ozone (O3) or carbon monoxide (CO) are large and costly, they are usually only sparsely distributed throughout a city. Hence, the official monitoring sites are not sufficient to investigate whether small-scale variability and its integrated effects are captured well by models. Smart networks consisting of small low-cost air pollutant sensors have the ability to provide the required grid density and are therefore the tool of choice when it comes to setting up or validating urban modeling frameworks. Such sensor networks have been established and run by several groups, achieving spatial and temporal high-resolution concentration maps [1, 2].After having conducted a measurement campaign in 2016 to create a high-resolution NO2 concentration map for Munich [3], we are currently setting up a low-cost sensor network to measure NOx, PM, O3 and CO concentrations as well as meteorological parameters [4]. The sensors are stand-alone, so that they do not demand mains supply, which gives us a high flexibility in their deployment. Validating air quality models not only requires dense but also high-accuracy measurements. Therefore, we will calibrate our sensor nodes on a weekly basis using a mobile reference instrument and apply the gathered sensor data to a Machine Learning model of the sensor nodes. This will help minimize the often occurring drawbacks of low-cost sensors such as sensor drift, environmental influences and sensor cross sensitivities.&#160;[1] Bigi, A., Mueller, M., Grange, S. K., Ghermandi, G., and Hueglin, C.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717&#8211;3735, https://doi.org/10.5194/amt-11-3717-2018, 2018[2] Kim, J., Shusterman, A. A., Lieschke, K. J., Newman, C., and Cohen, R. C.: The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors, Atmos. Meas. Tech., 11, 1937&#8211;1946, https://doi.org/10.5194/amt-11-1937-2018, 2018[3] Zhu, Y., Chen, J., Bi, X., Kuhlmann, G., Chan, K. L., Dietrich, F., Brunner, D., Ye, S., and Wenig, M.: Spatial and temporal representativeness of point measurements for nitrogen dioxide pollution levels in cities, Atmos. Chem. Phys., 20, 13241&#8211;13251, https://doi.org/10.5194/acp-20-13241-2020, 2020[4] Zollitsch, D., Chen, J., Dietrich, F., Voggenreiter, B., Setili, L., and Wenig, M.: Low-Cost Air Quality Sensor Network in Munich, EGU General Assembly 2020, Online, 4&#8211;8 May 2020, EGU2020-19276, https://doi.org/10.5194/egusphere-egu2020-19276, 2020

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FRI0531 AIR POLLUTANTS AND DEVELOPMENT OF INTERSTITIAL LUNG DISEASE IN PATIENTS WITH AUTOIMMUNE DISEASES

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.204 ◽

2020 ◽

Vol 79 (Suppl 1) ◽

pp. 865.1-865

Author(s):

H. H. Chen ◽

W. C. Chao ◽

Y. H. Chen ◽

D. Y. Chen ◽

C. H. Lin

Keyword(s):

Air Quality ◽

Autoimmune Diseases ◽

Air Pollutants ◽

Air Pollutant ◽

Air Quality Monitoring ◽

Newly Diagnosed ◽

Science Technology ◽

Air Quality Monitoring Stations ◽

The Impact ◽

Modest Level

Background:Interstitial lung disease (ILD) is characterized by progressive inflammation and fibrosis, and accumulating evidence have shown that exposure to air pollutants was associated with the development of ILD. Autoimmune diseases are highly correlated with ILD, including connective tissue disease-associated ILD (CTD-ILD) as well as interstitial pneumonia with autoimmune features (IPAF), and the development of ILD is a crucial cause of morbidity and mortality in patients with autoimmune diseases. One recent Taiwanese study reported that exposure to air pollutants was associated with incident systemic lupus erythematosus (SLE). However, the impact of air pollutants on the development of ILD among patients with autoimmune diseases remains unknown.Objectives:The study aimed to address the impact of accumulating exposure to air pollutant above moderate level, defined by Air Quality Index (AQI) value higher than 50, on the development of ILD in patients with autoimmune diseases including SLE, rheumatoid arthritis (RA) and primary Sjögren’s syndrome (SS).Methods:We used a National Health Insurance Research Database in Taiwan to enroll patients with SLE (International Classification of Diseases (ICD)-9 code 710.0, n=13,211), RA (ICD-9 code 714.0 and 714.30–714.33, n=32,373), and primary SS (ICD-9 code, 710.0, n=15,246) between 2001 and 2013. We identified newly diagnosed ILD cases (ICD-code 515) between 2012 and 2013 and selected age, sex, disease duration and index-year matched (1:4) patients as non-ILD controls. The hourly levels of air pollutants one year prior to the index-date were obtained from 60 air quality monitoring stations across Taiwan, and the air pollutants in the present study consisted of particulate matter <2.5 μm in size (PM2.5), particulate matter <10 μm in size (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2) and ozone (O3). We used a spatio-temporal model built by a deep-learning mechanism to estimate levels of air pollutants at 374 residential locations based on data of 3 air quality monitoring stations near the location (8). Notably, we used cumulative exposed hours to air pollutants higher than modest level, defined by AQI criteria, given that daily mean level of air pollutants might possibly underestimate the triggered inflammatory effect by a temporary exposure of high-level air pollutant. A conditional logistic regression was used to determine the association between exposure to air pollutant and the development of ILD, adjusting age, gender, Charlson Comorbidity Index (CCI), urbanization, family income, and medications for autoimmune diseases.Results:A total of 272 patients with newly diagnosed ILD were identified among patients with autoimmune diseases, including 39 with SLE, 135 with RA, and 98 with primary SS. We found that the duration of exposure to PM 2.5 higher than modest level was associated with the risk of ILD development in patients with SS (adjOR 1.07, 95% CI 1.01–1.13), and similar trends were also found in patients with SLE (adjOR 1.03, 95% CI 0.95–1.12) and RA (adjOR 1.03, 95% CI 0.99–1.07). Intriguingly, we observed an inverse correlation between the duration of exposure to O3 and the development of ILD in patients with SS (adjOR 0.83, 95% CI 0.70–0.99); however, the finding was not found in patients with SLE (adjOR 1.13, 95% CI 0.92–1.37) and RA (adjOR 0.98, 95% CI 0.87–1.11).Conclusion:In conclusion, we identified that longer exposure to PM2.5 higher than modest level tended to be associated with the development of ILD in patients with autoimmune diseases, mainly SS.References:[1] Araki T, Putman RK, Hatabu H, Gao W, Dupuis J, Latourelle JC, et al. Development and Progression of Interstitial Lung Abnormalities in the Framingham Heart Study. Am J Respir Crit Care Med 2016;194:1514-1522.[2] Tang KT, Tsuang BJ, Ku KC, Chen YH, Lin CH, Chen DY. Relationship between exposure to air pollutants and development of systemic autoimmune rheumatic diseases: a nationwide population-based case-control study. Ann Rheum Dis 2019;78:1288-1291.Disclosure of Interests:Hsin-Hua Chen: None declared, Wen-Cheng Chao: None declared, Yi-Hsing Chen Grant/research support from: Taiwan Ministry of Science and Technology, Taiwan Department of Health, Taichung Veterans General Hospital, National Yang-Ming University, GSK, Pfizer, BMS., Consultant of: Pfizer, Novartis, Abbvie, Johnson & Johnson, BMS, Roche, Lilly, GSK, Astra& Zeneca, Sanofi, MSD, Guigai, Astellas, Inova Diagnostics, UCB, Agnitio Science Technology, United Biopharma, Thermo Fisher, Gilead., Paid instructor for: Pfizer, Novartis, Johnson & Johnson, Roche, Lilly, Astra& Zeneca, Sanofi, Astellas, Agnitio Science Technology, United Biopharma., Speakers bureau: Pfizer, Novartis, Abbvie, Johnson & Johnson, BMS, Roche, Lilly, GSK, Astra& Zeneca, Sanofi, MSD, Guigai, Astellas, Inova Diagnostics, UCB, Agnitio Science Technology, United Biopharma, Thermo Fisher, Gilead., Der-Yuan Chen: None declared, Ching-Heng Lin: None declared

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Long-Term Assessment of Air Quality and Identification of Aerosol Sources at Setúbal, Portugal

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17155447 ◽

2020 ◽

Vol 17 (15) ◽

pp. 5447 ◽

Cited By ~ 1

Author(s):

Alexandra Viana Silva ◽

Cristina M. Oliveira ◽

Nuno Canha ◽

Ana Isabel Miranda ◽

Susana Marta Almeida

Keyword(s):

Air Quality ◽

Urban Areas ◽

Air Pollutants ◽

Air Pollutant ◽

Urban Traffic ◽

Anthropogenic Sources ◽

World Health ◽

Mitigation Measures ◽

Guideline Values ◽

Health Organization

Understanding air pollution in urban areas is crucial to identify mitigation actions that may improve air quality and, consequently, minimize human exposure to air pollutants and their impact. This study aimed to assess the temporal evolution of the air quality in the city of Setúbal (Portugal) during a time period of 10 years (2003–2012), by evaluating seasonal trends of air pollutants (PM10, PM2.5, O3, NO, NO2 and NOx) measured in nine monitoring stations. In order to identify emission sources of particulate matter, PM2.5 and PM2.5–10 were characterized in two different areas (urban traffic and industrial) in winter and summer and, afterwards, source apportionment was performed by means of Positive Matrix Factorization. Overall, the air quality has been improving over the years with a decreasing trend of air pollutant concentration, with the exception of O3. Despite this improvement, levels of PM10, O3 and nitrogen oxides still do not fully comply with the requirements of European legislation, as well as with the guideline values of the World Health Organization (WHO). The main anthropogenic sources contributing to local PM levels were traffic, industry and wood burning, which should be addressed by specific mitigation measures in order to minimize their impact on the local air quality.

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Ozone in Spain's National Parks and Protected Forests

The Scientific World JOURNAL ◽

10.1100/tsw.2007.8 ◽

2007 ◽

Vol 7 ◽

pp. 67-77 ◽

Cited By ~ 9

Author(s):

María J. Sanz ◽

Francisco Sanz ◽

Vicent Calatayud ◽

Gerardo Sanchez-Peña

Keyword(s):

Air Quality ◽

Protected Areas ◽

National Parks ◽

Canary Islands ◽

Air Pollutants ◽

Air Pollutant ◽

Passive Samplers ◽

Remote Areas ◽

Parks And Protected Areas ◽

Ozone Levels

In general, it is difficult to measure air pollutant concentrations in remote areas, as they are mostly national parks and protected areas. Passive samplers provide an accurate and inexpensive method for measuring cumulative exposures of different air pollutants. They have been used to collect ozone data in both laboratory and field at different geographical scales. The objective of the present study is to fill the knowledge gap regarding air quality in remote areas of Spain, such as national parks and protected areas. Because there were no systematic data sets on the main air pollutants that could affect these areas, an air quality measurement network was established between 2001 and 2004 on 19 locations inside Spanish national parks and protected areas. The data collected suggest that ozone levels in mountainous areas are high enough to affect sensitive vegetation. Most of the locations registered moderate-to-high ozone levels, with important interannual variability. Altitudinal ozone gradients were observed in most of the parks with complex topography due to the establishment of local circulations that incorporate polluted air masses from polluted airsheds or even long-range transport (i.e., Canary Islands). Different latitude-dependent, yearly cycles were also observed, showing two, one, or no clear peaks depending on the region. These findings extend to the most southerly locations, except in the Canary Islands, where pollution transported from other regions in the upper transport layers probably led to the high concentrations observed.

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Land use regression modelling of ambient air pollutants in Hong Kong

10.5194/egusphere-egu2020-12648 ◽

2020 ◽

Author(s):

Zhiyuan Li ◽

Steve Hung Lam Yim ◽

Kin-Fai Ho

Keyword(s):

Land Use ◽

Hong Kong ◽

Air Quality ◽

Air Pollutants ◽

Ambient Air ◽

Air Pollutant ◽

High Rise ◽

Pollutant Concentrations ◽

Air Quality Measurements ◽

Air Pollutant Concentrations

Land use regression (LUR) models estimate air pollutant concentrations for areas without air quality measurements, which provides valuable information for exposure assessment and epidemiological studies. In the present study, we developed LUR models for ambient air pollutants in Hong Kong, China, a typical high-density and high-rise city. Air quality measurements at sixteen air quality monitoring stations, operated by the Hong Kong Environmental Protection Department, were collected. Moreover, five categories of predictor variables, including population distribution, traffic emissions, land use variables, urban/building morphology, and meteorological parameters, were employed to establish the LUR models of various air pollutants. Then the spatial distribution of air pollutant concentrations at 1 km &#215; 1 km grid cells were plotted. Taking fine particle (PM2.5) as an example, the developed LUR model explained 89% of variability of PM2.5 concentrations, with a leave-one-out-cross-validation R2 of 0.64. LUR modelling results for other air pollutants will be presented. In addition, further improvements on the development of LUR models will be discussed. This study can help to assess long-term exposures to air pollutants for high-density and high-rise urban areas like Hong Kong.

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