The Influence of Shallow Foehn on Atmospheric Diffusion Conditions and Air Quality over Urumqi in Winter

2020 ◽  
Author(s):  
Zhao keming
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Air Pollutants ◽  
Meteorological Data ◽  
Inversion Layer ◽  
Mixing Layer ◽  
Radar Data ◽  
Air Pollutant ◽  
Pollution Level ◽  
Atmospheric Diffusion

<p>Using hourly air pollutants concentration from six environmental monitor stations, meteorological data and wind profile radar data in winter during 2013-2015, the influences of shallow foehn on diffusion conditions and air pollution concentration over Urumqi were analyzed. The results showed the occurrence frequency of shallow foehn was 57.3% in Urumqi in winter. The flow depth, base height and top height of shallow foehn were about 1500 m, 600 m and 2100 m, respectively. The maximum mixing layer depth, the inversion depth, the temperature difference between the top and bottom of inversion layer on foehn days were 200 m lower, 344m thicker and 4.4℃ higher than the corresponding values on non-foehn days, respectively. However, the differences of wind speed and inversion intensity between on foehn days and on non-foehn days were slight. Also, the frequency of each pollution level on foehn days was higher than on non-foehn days with extra frequency of 18% from level Ⅲ to level Ⅵ. Moreover, there was foehn existence on days with air pollution level Ⅵ. Except for O<sub>3</sub>, the other five air pollutant concentrations at each environmental station on foen days were all higher than on non-foehn days but with similar diurnal variation. The spatial distributions of six air pollutants on foehn days and non-foehn days were almost same. Overall, the air quality at south urban area was relative excellent than other areas.</p>

scholarly journals Pre-monsoon air quality over Lumbini, a world heritage site along the Himalayan foothills

2016 ◽  
Author(s):  
Dipesh Rupakheti ◽  
Bhupesh Adhikary ◽  
Puppala S. Praveen ◽  
Maheswar Rupakheti ◽  
Shichang Kang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Forest Fires ◽  
Air Pollutants ◽  
Transport Model ◽  
World Heritage ◽  
World Heritage Site ◽  
Pollution Level ◽  
Pollution Transport ◽  
Heritage Site

Abstract. Lumbini, in southern Nepal, is a UNESCO world heritage site of universal value as the birthplace of Buddha. Poor air quality in Lumbini and surrounding regions is a great concern for public health as well as for preservation, protection and promotion of Buddhist heritage and culture. We present here results from measurements of ambient concentrations of key air pollutants (PM, BC, CO, O3) in Lumbini, first of its kind for Lumbini, conducted during an intensive measurement period of three months (April–June 2013) in the pre-monsoon season. The measurements were carried out as a part of the international air pollution measurement campaign; SusKat-ABC (Sustainable Atmosphere for the Kathmandu Valley – Atmospheric Brown Clouds). The ranges of hourly average concentrations were: PM10: 10.5–604.0 µg m−3, PM2.5: 6.1–272.2 µg m−3; BC: 0.3–30.0 µg m−3; CO: 125.0–1430.0 ppbv; and O3: 1.0–118.1 ppbv. These levels are comparable to other very heavily polluted sites throughout South Asia. The 24-h average PM2.5 and PM10 concentrations exceeded the WHO guideline very frequently (94 % and 85 % of the sampled period, respectively), which implies significant health risks for the residents and visitors in the region. These air pollutants exhibited clear diurnal cycles with high values in the morning and evening. During the study period, the worst air pollution episodes were mainly due to agro-residue burning and regional forest fires combined with meteorological conditions conducive of pollution transport to Lumbini. Fossil fuel combustion also contributed significantly, accounting for more than half of the ambient BC concentration according to aerosol spectral light absorption coefficients obtained in Lumbini. WRF-STEM, a regional chemical transport model, was used to simulate the meteorology and the concentrations of pollutants. The model was able to reproduce the variation in the pollutant concentrations well; however, estimated values were 1.5 to 5 times lower than the observed concentrations for CO and PM10 respectively. Regionally tagged CO tracers showed the majority of CO came from the upwind region of Ganges valley. The model was also used to examine the chemical composition of the aerosol mixture, indicating that organic carbon was the main constituent of fine mode PM2.5, followed by mineral dust. Given the high pollution level, there is a clear and urgent need for setting up a network of long-term air quality monitoring stations in the greater Lumbini region.

Changes of air pollutants’ concentrations in selected Romanian cities during the pandemic year 2020

2021 ◽  
Author(s):  
Gabriela Iorga ◽  
George-Bogdan Burghelea
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
European Commission ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Dust Particles ◽  
Spatial And Temporal Variation ◽  
Pollution Level ◽  
Data Set ◽  
Air Mass

<p>Present research contributes to scientific knowledge concerning spatial and temporal variation of major air pollutants with high resolution at the country scale bringing statistical information on concentrations of NOx, O<sub>3</sub>, CO, SO<sub>2</sub> and particulate matter with an aerodynamic diameter below 10 μm (PM<sub>10</sub>) and below 2.5 μm (PM<sub>2.5</sub>) during the pandemic year 2020 using an observational data set from the Romanian National Air Quality Network in seven selected cities spread out over the country. These cities have different level of development, play regional roles, might have potential influence at European scale and they are expected to be impacted by different pollution sources. Among them, three cities (Bucharest, Brașov, Iași) appear frequently on the list of the European Commission with reference to the infringement procedure that the European Commission launched against Romania in the period 2007-2020 regarding air quality.</p><p>Air pollutant data was complemented with local meteorological parameters at each site (atmospheric pressure, relative humidity, temperature, global solar radiation, wind speed and direction). Statistics of air pollutants provide us with an overview of air pollution in main Romanian cities.  Correlations between meteorological parameters and ambient pollutant levels were analyzed. Lowest air pollution levels were measured during the lockdown period in spring, as main traffic and non-essential activities were severely restricted. Among exceptions were the construction activities that were not interrupted. During 2020, some of selected cities experienced few pollution episodes which were due to dust transport from Sahara desert. However, in Bucharest metropolitan area, some cases with high pollution level were found correlated with local anthropogenic activity namely, waste incinerations. Air mass origins were investigated for 72 hours back by computing the air mass backward trajectories using the HYSPLIT model. Dust load and spatial distribution of the aerosol optical depth with BSC-DREAM8b v2.0 and NMBM/BSC-Dust models showed the area with dust particles transport during the dust events.</p><p>The obtained results are important for investigations of sources of air pollution and for modeling of air quality.</p><p><strong> </strong></p><p><strong>Acknowledgment:</strong></p><p>The research leading to these results has received funding from the NO Grants 2014-2021, under Project contract no. 31/2020, EEA-RO-NO-2019-0423 project. NOAA Air Resources Laboratory for HYSPLIT transport model, available at READY website https://www.ready.noaa.gov  and the Barcelona dust forecast center for BSC-DREAM8b and NMBM/BSC-Dust models, available at:  https://ess.bsc.es/bsc-dust-daily-forecast are also acknowledged. The data regarding ground-based air pollution and meteorology by site was extracted from the public available Romanian National Air Quality Database, www.calitateaer.ro.</p>

scholarly journals DESIGN AND RESULTS OF AN AI-BASED FORECASTING OF AIR POLLUTANTS FOR SMART CITIES

2021 ◽  
Vol VIII-4/W1-2021 ◽  
pp. 89-96
Author(s):  
L. Petry ◽  
T. Meiers ◽  
D. Reuschenberg ◽  
S. Mirzavand Borujeni ◽  
J. Arndt ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Smart Cities ◽  
Meteorological Data ◽  
Weather Forecast ◽  
Ground Level ◽  
Urban Environments ◽  
Air Pollutant ◽  
Novel Approach

Abstract. This paper presents the design and the results of a novel approach to predict air pollutants in urban environments. The objective is to create an artificial intelligence (AI)-based system to support planning actors in taking effective and adequate short-term measures against unfavourable air quality situations. In general, air quality in European cities has improved over the past decades. Nevertheless, reductions of the air pollutants particulate matter (PM), nitrogen dioxide (NO2) and ground-level ozone (O3), in particular, are essential to ensure the quality of life and a healthy life in cities. To forecast these air pollutants for the next 48 hours, a sequence-to-sequence encoder-decoder model with a recurrent neural network (RNN) was implemented. The model was trained with historic in situ air pollutant measurements, traffic and meteorological data. An evaluation of the prediction results against historical data shows high accordance with in situ measurements and implicate the system’s applicability and its great potential for high quality forecasts of air pollutants in urban environments by including real time weather forecast data.

scholarly journals Temporal analysis of air pollutants in the most affected monsoon region

2021 ◽  
Vol 23 (1) ◽  
pp. 229
Author(s):  
Omar Kairan ◽  
Nur Nasehah Zainudin ◽  
Nurul Hasya Mohd Hanafiah ◽  
Nur Emylia Arissa Mohd Jafri ◽  
Fukayhah Fatiha @Suhami ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Air Pollutants ◽  
Monsoon Season ◽  
Secondary Data ◽  
Ambient Air ◽  
Temporal Analysis ◽  
Quality Standard ◽  
Temporal Variations ◽  
Air Pollutant

Air pollution has become an issue at all rates in the world. In Malaysia, there is a system is known as air quality index (API) used to indicate the overall air quality in the country where the air pollutants include or the new ambient air quality standard are sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3) and particulate matter with size less than 10 (PM10). The concentration levels of the air pollutants were said to be affected by the monsoon changes. Therefore, this study is conducted to examine the existence of temporal variations of each air pollutant then identify the differences of each air pollutants concentration in temporal variations. This study uses secondary data where data that has been retrieved from the Department of Environment (DOE) where it is data of air pollution specifically for Kota Bharu, kelantan records. Hierarchical agglomerative cluster analysis was conducted to group monthly air quality. As a conclusion, the study can conclude that the five air pollutants grouped into several different monthly clusters mostly representing the two main monsoon seasons. Mostly air pollutant varied accordingly towards the monsoon season. During the southwestern monsoon, air pollutant concentration tends to higher compare to the northeastern monsoon with mostly due to meteorological factors.

scholarly journals Pre-monsoon air quality over Lumbini, a world heritage site along the Himalayan foothills

2017 ◽  
Vol 17 (18) ◽  
pp. 11041-11063 ◽  
Author(s):  
Dipesh Rupakheti ◽  
Bhupesh Adhikary ◽  
Puppala Siva Praveen ◽  
Maheswar Rupakheti ◽  
Shichang Kang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Air Pollutants ◽  
World Heritage ◽  
World Heritage Site ◽  
Pollution Level ◽  
Pollution Transport ◽  
Gangetic Plain ◽  
Measurement Campaign ◽  
Heritage Site

Abstract. Lumbini, in southern Nepal, is a UNESCO world heritage site of universal value as the birthplace of Buddha. Poor air quality in Lumbini and surrounding regions is a great concern for public health as well as for preservation, protection and promotion of Buddhist heritage and culture. We present here results from measurements of ambient concentrations of key air pollutants (PM, BC, CO, O3) in Lumbini, first of its kind for Lumbini, conducted during an intensive measurement period of 3 months (April–June 2013) in the pre-monsoon season. The measurements were carried out as a part of the international air pollution measurement campaign; SusKat-ABC (Sustainable Atmosphere for the Kathmandu Valley – Atmospheric Brown Clouds). The main objective of this work is to understand and document the level of air pollution, diurnal characteristics and influence of open burning on air quality in Lumbini. The hourly average concentrations during the entire measurement campaign ranged as follows: BC was 0.3–30.0 µg m−3, PM1 was 3.6–197.6 µg m−3, PM2. 5 was 6.1–272.2 µg m−3, PM10 was 10.5–604.0 µg m−3, O3 was 1.0–118.1 ppbv and CO was 125.0–1430.0 ppbv. These levels are comparable to other very heavily polluted sites in South Asia. Higher fraction of coarse-mode PM was found as compared to other nearby sites in the Indo-Gangetic Plain region. The ΔBC ∕ ΔCO ratio obtained in Lumbini indicated considerable contributions of emissions from both residential and transportation sectors. The 24 h average PM2. 5 and PM10 concentrations exceeded the WHO guideline very frequently (94 and 85 % of the sampled period, respectively), which implies significant health risks for the residents and visitors in the region. These air pollutants exhibited clear diurnal cycles with high values in the morning and evening. During the study period, the worst air pollution episodes were mainly due to agro-residue burning and regional forest fires combined with meteorological conditions conducive of pollution transport to Lumbini. Fossil fuel combustion also contributed significantly, accounting for more than half of the ambient BC concentration according to aerosol spectral light absorption coefficients obtained in Lumbini. WRF-STEM, a regional chemical transport model, was used to simulate the meteorology and the concentrations of pollutants to understand the pollutant transport pathways. The model estimated values were ∼ 1. 5 to 5 times lower than the observed concentrations for CO and PM10, respectively. Model-simulated regionally tagged CO tracers showed that the majority of CO came from the upwind region of Ganges Valley. Model performance needs significant improvement in simulating aerosols in the region. Given the high air pollution level, there is a clear and urgent need for setting up a network of long-term air quality monitoring stations in the greater Lumbini region.

scholarly journals The Impact of Atmospheric Pollutants on Human Health and Economic Loss Assessment

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1628
Author(s):  
Houli Zhang ◽  
Shibing You ◽  
Miao Zhang ◽  
Difei Liu ◽  
Xuyan Wang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Human Health ◽  
Air Pollutants ◽  
Respiratory Diseases ◽  
Meteorological Data ◽  
Air Pollutant ◽  
Atmospheric Pollutants ◽  
Economic Losses ◽  
Air Pollution Control ◽  
The Impact

The impact of air pollution on human health is becoming increasingly severe, and economic losses are a significant impediment to economic and social development. This paper investigates the impact of air pollutants on the respiratory system and its action mechanism by using information on inpatients with respiratory diseases from two IIIA (highest) hospitals in Wuhan from 2015 to 2019, information on air pollutants, and meteorological data, as well as relevant demographic and economic data in China. This paper describes the specific conditions of air pollutant concentrations and respiratory diseases, quantifies the degree of correlation between the two, and then provides a more comprehensive assessment of the economic losses using descriptive statistical methods, the generalized additive model (GAM), cost of illness approach (COI), and scenario analysis. According to the findings, the economic losses caused by PM2.5, PM10, SO2, NO2, and CO exposure are USD 103.17 million, USD 70.54 million, USD 98.02 million, USD 40.35 million, and USD 142.38 million, for a total of USD 454.46 billion, or approximately 0.20% of Wuhan’s GDP in 2019. If the government tightens control of major air pollutants and meets the WHO-recommended criterion values, the annual evitable economic losses would be approximately USD 69.4 million or approximately 0.03% of Wuhan’s GDP in 2019. As a result, the relevant government departments must strengthen air pollution control to mitigate the impact of air pollution on population health and the associated economic losses.

scholarly journals Machine Learning-Based Forecast of Hemorrhagic Stroke Healthcare Service Demand considering Air Pollution

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Jian Chen ◽  
Hong Li ◽  
Li Luo ◽  
Yangyang Zhang ◽  
Fengyi Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Air Pollutants ◽  
Hemorrhagic Stroke ◽  
Meteorological Data ◽  
Area Under The Curve ◽  
Warm Season ◽  
Forecast Model ◽  
Healthcare Services

This study aimed to forecast the pattern of the demand for hemorrhagic stroke healthcare services based on air quality and machine learning. Hemorrhagic stroke, air quality, and meteorological data for 2016-2017 were obtained from the Longquanyi District of China, and the study included 1932 cases. Six machine learning methods were used to forecast the demand for hemorrhagic stroke healthcare services considering seasonality and a lag effect, and the average area under the curve was as high as 0.7971. Our results indicate that (1) the performance of forecasting during the warm season is significantly better than that in the cold season, (2) considering air pollution would improve the performance of forecasting the demand for hemorrhagic stroke healthcare services using machine learning, (3) the association between the demand for hemorrhagic stroke healthcare services and air pollutants is linear to some extent, and (4) it is feasible to use short-term concentrations of air pollutants to forecast the demand for hemorrhagic stroke healthcare services. This practical forecast model could provide an advance warning regarding the potentially high numbers of hemorrhagic stroke admissions to medical institutions, thus allowing time to implement an appropriate response to the increase in patient volumes.

scholarly journals Prediction of the Level of Air Pollution During Wildfires Using Machine Learning Classification Methods

2020 ◽  
Vol 6 (2) ◽  
pp. 115-123
Author(s):  
Syed Mohammed Khalid ◽  
Raini Hassan
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Forest Fires ◽  
Meteorological Data ◽  
Ambient Air ◽  
Air Pollutant ◽  
Optical Data ◽  
Gradient Boosting ◽  
Agricultural Field ◽  
Machine Learning Classification

 The recent increase of forest fires due to agricultural field burning in the South East Asian region has led to haze episodes in Malaysia which contributed to the increasing number of hospital visits for treatments related to respiratory diseases. With the increase of air pollution, it becomes a necessity to attempt at investigating and predicting the air pollution levels, which would in turn which would lead to proper strategies so untimely effects to human health can be kept at a minimum. The Air Pollutant Index is used to identify and classify the ambient air quality status, However the lack of ground air quality monitors which compute the API generally leads to unreliable warning information. Recent studies indicate that data retrieved from remote sensing satellites is now an emerging alternative for air quality prediction at the ground level, hence this research aims to use satellite-based data to predict the air quality of East Malaysian cities with the help of different classification algorithms. Aerosol Optical data, Meteorological data and Fire data were collected from different satellite sources, two algorithms were selected and modelled. The two algorithms which were implemented, were Random Forest and Gradient Boosting, when trained and validated they both algorithms performed reasonably well with an accuracy 0.89 and 0.85 respectively, for the city of Kuching.

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

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.

Characteristics and Causes of Air Pollution in Lanzhou, China

2012 ◽  
Vol 260-261 ◽  
pp. 808-814
Author(s):  
Yu Xia Ma
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Air Pollutants ◽  
Meteorological Data ◽  
Urban Air Pollution ◽  
National Income ◽  
Pollution Index ◽  
Meteorological Factor ◽  
Economic Losses ◽  
Air Pollution Index

Ten of the top 20 polluted cities are in China, while Lanzhou is the most famous one in the ten. Relevant foreign researches show that urban air pollution generated economic losses reached the city of 3% of national income. In this paper, air pollutants, air pollution index (API for short) and meteorological data of 2000-2008 are analyzed using statistical methods and results show that: (1) Air pollution index (API) in Lanzhou shows an obvious seasonal change with high value in winter and low in summer.(2) Its primary air pollutants are particulate pollutants, in total 3113 days during 2000-2008, only 96days of air quality are good and taking 3.08%; there are severe polluted 168 days, moderate polluted 114 days and light polluted 1106 days.(3) Primary pollution particles of 2928 days are inhalable particles, accounting for 94.06%. Second pollution is SO2 and taking 2.97%. (4) Seasonal distribution of pollution, severe pollution and moderate pollution occurs mostly in winter and spring quarters. Seasonal variation shows the number of severe polluted days in the four months of Jan, March, April, and Dec account for 78.6% of total severe polluted days during 2000-2008. The most severe polluted and second polluted days mainly occurred in winter and spring, closely related with winter house heating and spring dust storm. The number of excellent air quality days is more in the rainy days, indicating that deposition of pollutants or the dilution effect precipitation is apparent. (5) There are close relationship between API and meteorological factor such as precipitation, humidity and wind speed.

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