Research on Environment Materials with Variation Rule and Characteristic of Air Pollutants in Nanchang City

2014 ◽  
Vol 540 ◽  
pp. 247-250
Author(s):  
Xi Feng Yan ◽  
Jing Lan Wang ◽  
You Hua Zhou ◽  
Feng Liu
Keyword(s):  
Correlation Analysis ◽  
Diurnal Variation ◽  
Air Pollutants ◽  
Air Pollutant ◽  
Monitoring Data ◽  
Annual Variability ◽  
Changing Trends ◽  
Variation Rule

An analysis of the change regularity of the air pollutant in Nanchang city in this paper. According to the monitoring data of SO2, NO2 and PM10 from 1996 to 2012,the time characteristics and causes of SO2, NO2 and PM10 pollution were analyzed ,and the changing trends of the concentration of SO2, NO2 and PM10 in17 years were described by using Correlation analysis. The result shows that Nanchang air pollutants have the characteristics of obvious inter-annual variability, Rose and diurnal variation.

Analysis of Acid Rain and the Main Effect Factors in Nanchang City

2014 ◽  
Vol 1078 ◽  
pp. 154-157
Author(s):  
Xi Feng Yan ◽  
Jing Lan Wang ◽  
You Hua Zhou ◽  
Feng Liu
Keyword(s):  
Correlation Analysis ◽  
Diurnal Variation ◽  
Acid Rain ◽  
Air Pollutants ◽  
Monitoring Data ◽  
Effect Factors ◽  
Annual Variability ◽  
Changing Trends ◽  
Main Effect

The performance of acid rain at Nanchang, China was studied in this paper. According to the monitoring data of rain from 2003 to 2012, the time characteristics and causes of the acid rain were analyzed, and the changing trends of the concentration in10 years were described by using Correlation analysis. The result shows that Nanchang air pollutants have the characteristics of obvious inter-annual variability, Rose and diurnal variation.

Analysis of the Atmospheric Visibility in Nanchang City and the Main Effect Factors

2014 ◽  
Vol 908 ◽  
pp. 404-407
Author(s):  
Jing Lan Wang ◽  
Xi Feng Yan ◽  
Su He ◽  
Feng Liu
Keyword(s):  
Relative Humidity ◽  
Diurnal Variation ◽  
Negative Correlation ◽  
Air Pollutants ◽  
Air Pollutant ◽  
Effect Factors ◽  
Atmospheric Visibility ◽  
Main Effect ◽  
Meteorological Elements ◽  
The Relationship

The performance of atmospheric visibility at Nanchang, China was studied in this paper. The relationship between atmospheric visibility and the meteorological elements was analyzed using statistical method based on those data of Nanchang atmospheric visibility and the observed record of ground meteorological elements from 1990 to 2012. The relationship between the major air pollutants and the atmospheric visibility also was researched according to the air pollutant monitoring data from 2006 to 2012. The result shows that Nanchang atmospheric visibility has the characteristics of obvious inter-annual variability, Rose and diurnal variation; the visibility was significantly negatively correlated to relative humidity; there was weakly negative correlation between the air pollutants SO2, NOx concentration and the visibility.

Emissions Responsibility and Relative Importance of the Productive Sectors in Turkey: 2002-2011

2014 ◽  
Author(s):  
Celal Taşdoğan ◽  
Bilgen Taşdoğan
Keyword(s):  
Air Pollutants ◽  
Economic Value ◽  
Value Added ◽  
High Growth ◽  
Air Pollutant ◽  
Sustainable Growth ◽  
Environmental Damage ◽  
Relative Importance ◽  
Input Output ◽  
Key Sectors

Turkey has realized high growth rates during the period of 2002-2011, except in 2008 and 2009 years. It is thought that the rapidly growing in the country may cause a lot of environmental damage, especially air pollution problems. In other words, the productive sectors have produced two outputs which are economic value added and air pollutants. This study used input output matrixes are to find out the strategically important sectors as it is known key sectors and weak sectors caused the environmental effects in the country. For this purpose, it has been tried to investigate air pollutant quantities which caused by the production process of the sectors in the period of 2002-2011 and performed the input-output tables for Turkey constructed in the World Input Output Database (WIOD) Project. These input-output tables include the emission satellite accounts, which are CO2 emissions and other air pollutants, respectively N2O, CH4, N2O, NOx, SOx, CO, NMVOC and NH3, disaggregated for the 34 sectors. It is expected that the outcomes of the study may contribute to sustainable growth debates and environmental policy implementations in Turkey.

scholarly journals Relationship between Air Pollutant Exposure and Gynecologic Cancer Risk

2021 ◽  
Vol 18 (10) ◽  
pp. 5353
Author(s):  
Qiwei Yu ◽  
Liqiang Zhang ◽  
Kun Hou ◽  
Jingwen Li ◽  
Suhong Liu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Cancer Risk ◽  
Air Pollutants ◽  
Gynecological Cancer ◽  
Gynecologic Cancer ◽  
Ambient Air ◽  
Air Pollutant ◽  
Ambient Air Pollution ◽  
Short Term Exposure ◽  
Increased Risk

Exposure to air pollution has been suggested to be associated with an increased risk of women’s health disorders. However, it remains unknown to what extent changes in ambient air pollution affect gynecological cancer. In our case–control study, the logistic regression model was combined with the restricted cubic spline to examine the association of short-term exposure to air pollution with gynecological cancer events using the clinical data of 35,989 women in Beijing from December 2008 to December 2017. We assessed the women’s exposure to air pollutants using the monitor located nearest to each woman’s residence and working places, adjusting for age, occupation, ambient temperature, and ambient humidity. The adjusted odds ratios (ORs) were examined to evaluate gynecologic cancer risk in six time windows (Phase 1–Phase 6) of women’s exposure to air pollutants (PM2.5, CO, O3, and SO2) and the highest ORs were found in Phase 4 (240 days). Then, the higher adjusted ORs were found associated with the increased concentrations of each pollutant (PM2.5, CO, O3, and SO2) in Phase 4. For instance, the adjusted OR of gynecological cancer risk for a 1.0-mg m−3 increase in CO exposures was 1.010 (95% CI: 0.881–1.139) below 0.8 mg m−3, 1.032 (95% CI: 0.871–1.194) at 0.8–1.0 mg m−3, 1.059 (95% CI: 0.973–1.145) at 1.0–1.4 mg m−3, and 1.120 (95% CI: 0.993–1.246) above 1.4 mg m−3. The ORs calculated in different air pollution levels accessed us to identify the nonlinear association between women’s exposure to air pollutants (PM2.5, CO, O3, and SO2) and the gynecological cancer risk. This study supports that the gynecologic risks associated with air pollution should be considered in improved public health preventive measures and policymaking to minimize the dangerous effects of air pollution.

scholarly journals The effects of energy paths and emission controls and standards on future trends in China's emissions of primary air pollutants

2014 ◽  
Vol 14 (17) ◽  
pp. 8849-8868 ◽  
Author(s):  
Y. Zhao ◽  
J. Zhang ◽  
C. P. Nielsen
Keyword(s):  
Air Pollutants ◽  
Control Strategies ◽  
Emission Control ◽  
Pollution Prevention ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Carbonaceous Aerosols ◽  
Emission Controls ◽  
Control Devices ◽  
Anthropogenic Pollutant

Abstract. To examine the efficacy of China's actions to control atmospheric pollution, three levels of growth of energy consumption and three levels of implementation of emission controls are estimated, generating a total of nine combined activity-emission control scenarios that are then used to estimate trends of national emissions of primary air pollutants through 2030. The emission control strategies are expected to have more effects than the energy paths on the future emission trends for all the concerned pollutants. As recently promulgated national action plans of air pollution prevention and control (NAPAPPC) are implemented, China's anthropogenic pollutant emissions should decline. For example, the emissions of SO2, NOx, total suspended particles (TSP), PM10, and PM2.5 are estimated to decline 7, 20, 41, 34, and 31% from 2010 to 2030, respectively, in the "best guess" scenario that includes national commitment of energy saving policy and implementation of NAPAPPC. Should the issued/proposed emission standards be fully achieved, a less likely scenario, annual emissions would be further reduced, ranging from 17 (for primary PM2.5) to 29% (for NOx) declines in 2015, and the analogue numbers would be 12 and 24% in 2030. The uncertainties of emission projections result mainly from the uncertain operational conditions of swiftly proliferating air pollutant control devices and lack of detailed information about emission control plans by region. The predicted emission trends by sector and chemical species raise concerns about current pollution control strategies: the potential for emissions abatement in key sectors may be declining due to the near saturation of emission control devices use; risks of ecosystem acidification could rise because emissions of alkaline base cations may be declining faster than those of SO2; and radiative forcing could rise because emissions of positive-forcing carbonaceous aerosols may decline more slowly than those of SO2 emissions and thereby concentrations of negative-forcing sulfate particles. Expanded control of emissions of fine particles and carbonaceous aerosols from small industrial and residential sources is recommended, and a more comprehensive emission control strategy targeting a wider range of pollutants (volatile organic compounds, NH3 and CO, etc.) and taking account of more diverse environmental impacts is also urgently needed.

scholarly journals Association between Air Pollution and the Development of Rheumatic Disease: A Systematic Review

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Gavin Sun ◽  
Glen Hazlewood ◽  
Sasha Bernatsky ◽  
Gilaad G. Kaplan ◽  
Bertus Eksteen ◽  
...  
Keyword(s):  
Air Pollution ◽  
Rheumatic Diseases ◽  
Air Pollutants ◽  
Population Sample ◽  
Case Control ◽  
Air Pollutant ◽  
Case Control Studies ◽  
Mesh Terms ◽  
Increased Risk ◽  
American Children

Objective. Environmental risk factors, such as air pollution, have been studied in relation to the risk of development of rheumatic diseases. We performed a systematic literature review to summarize the existing knowledge.Methods. MEDLINE (1946 to September 2016) and EMBASE (1980 to 2016, week 37) databases were searched using MeSH terms and keywords to identify cohort, case-control, and case cross-over studies reporting risk estimates for the development of select rheumatic diseases in relation to exposure of measured air pollutants (n=8). We extracted information on the population sample and study period, method of case and exposure determination, and the estimate of association.Results. There was no consistent evidence of an increased risk for the development of rheumatoid arthritis (RA) with exposure to NO2, SO2, PM2.5, or PM10. Case-control studies in systemic autoimmune rheumatic diseases (SARDs) indicated higher odds of diagnosis with increasing PM2.5exposure, as well as an increased relative risk for juvenile idiopathic arthritis (JIA) in American children <5.5 years of age. There was no association with SARDs and NO2exposure.Conclusion. There is evidence for a possible association between air pollutant exposures and the development of SARDs and JIA, but relationships with other rheumatic diseases are less clear.

scholarly journals Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables

2020 ◽  
Vol 17 (6) ◽  
pp. 2025
Author(s):  
Laura Goulier ◽  
Bastian Paas ◽  
Laura Ehrnsperger ◽  
Otto Klemm
Keyword(s):  
Air Pollutants ◽  
Air Pollutant ◽  
Urban Air ◽  
Pollutant Concentrations ◽  
Day Of The Week ◽  
Artificial Neural ◽  
Input Variables ◽  
Air Pollutant Concentrations ◽  
Artificial Neural Network Ann ◽  
Performance Results

Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO2, NH3, NO, NO2, NOx, O3, PM1, PM2.5, PM10 and PN10) in a street canyon in Münster using an artificial neural network (ANN) approach. Special attention was paid to comparing three predictor options representing the traffic volume: we included acoustic sound measurements (sound), the total number of vehicles (traffic), and the hour of the day and the day of the week (time) as input variables and then compared their prediction powers. The models were trained, validated and tested to evaluate their performance. Results showed that the predictions of the gaseous air pollutants NO, NO2, NOx, and O3 reveal very good agreement with observations, whereas predictions for particle concentrations and NH3 were less successful, indicating that these models can be improved. All three input variable options (sound, traffic and time) proved to be suitable and showed distinct strengths for modelling various air pollutant concentrations.

scholarly journals Analysis of Temporal Variations of Air Pollutant Concentrations in Ogoni Area, Niger Delta Region, Nigeria

2021 ◽  
pp. 63-73
Author(s):  
B. Yorkor ◽  
T. G. Leton ◽  
J. N. Ugbebor
Keyword(s):  
Air Pollution ◽  
Temporal Variability ◽  
Niger Delta ◽  
Air Pollutants ◽  
Temporal Variations ◽  
Time Of Day ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Hourly Data ◽  
Air Pollutant Concentrations

This study investigated the temporal variations of air pollutant concentrations in Ogoni area, Niger Delta, Nigeria. The study used hourly data measured over 8 hours for 12 months at selected locations within the area. The analyses were based on time series and time variations techniques in Openair packages of R programming software. The variations of air pollutant concentrations by time of day and days of week were simulated. Hours of the day, days of the week and monthly variations were graphically simulated. Variations in the mean concentrations of air pollutants by time were determined at 95 % confidence intervals. Sulphur dioxide (SO2), Nitrogen dioxide (NO2), ground level Ozone (O3) and fine particulate matter (PM2.5) concentrations exceeded permissible standards. Air pollutant concentrations showed increase in January, February, November and December compared to other months. Simulation showed that air pollutants varied significantly by hours-of-the-day and days-of-the-week and months-of-the-year. Analysis of temporal variability revealed that air pollutant concentrations increased during weekdays and decreased during weekends. The temporal variability of air pollutants in Ogoni area showed that anthropogenic activities were the main sources of air pollution in the area, therefore further studies are required to determine air pollutant dispersion pattern and evaluation the potential sources of air pollution in the area.

scholarly journals Associations of Ambient Air Pollutants and Meteorological Factors With COVID-19 Transmission in 31 Chinese Provinces: A Time Series Study

2021 ◽  
Vol 58 ◽  
pp. 004695802110602
Author(s):  
Han Cao ◽  
Bingxiao Li ◽  
Tianlun Gu ◽  
Xiaohui Liu ◽  
Kai Meng ◽  
...  
Keyword(s):  
Air Pollution ◽  
Relative Humidity ◽  
Wind Velocity ◽  
Air Pollutants ◽  
Meteorological Factors ◽  
Meta Analysis ◽  
Ambient Air ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

Evidence regarding the effects of environmental factors on COVID-19 transmission is mixed. We aimed to explore the associations of air pollutants and meteorological factors with COVID-19 confirmed cases during the outbreak period throughout China. The number of COVID-19 confirmed cases, air pollutant concentrations, and meteorological factors in China from January 25 to February 29, 2020, (36 days) were extracted from authoritative electronic databases. The associations were estimated for a single-day lag as well as moving averages lag using generalized additive mixed models. Region-specific analyses and meta-analysis were conducted in 5 selected regions from the north to south of China with diverse air pollution levels and weather conditions and sufficient sample size. Nonlinear concentration–response analyses were performed. An increase of each interquartile range in PM2.5, PM10, SO2, NO2, O3, and CO at lag4 corresponded to 1.40 (1.37–1.43), 1.35 (1.32–1.37), 1.01 (1.00–1.02), 1.08 (1.07–1.10), 1.28 (1.27–1.29), and 1.26 (1.24–1.28) ORs of daily new cases, respectively. For 1°C, 1%, and 1 m/s increase in temperature, relative humidity, and wind velocity, the ORs were 0.97 (0.97–0.98), 0.96 (0.96–0.97), and 0.94 (0.92–0.95), respectively. The estimates of PM2.5, PM10, NO2, and all meteorological factors remained significantly after meta-analysis for the five selected regions. The concentration–response relationships showed that higher concentrations of air pollutants and lower meteorological factors were associated with daily new cases increasing. Higher air pollutant concentrations and lower temperature, relative humidity and wind velocity may favor COVID-19 transmission. Controlling ambient air pollution, especially for PM2.5, PM10, NO2, may be an important component of reducing risk of COVID-19 infection. In addition, as winter months are arriving in China, the meteorological factors may play a negative role in prevention. Therefore, it is significant to implement the public health control measures persistently in case another possible pandemic.

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

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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