scholarly journals Geostatistical integration and uncertainty in pollutant concentration surface under preferential sampling

2016 ◽  
Vol 11 (1) ◽  
Author(s):  
Laura Grisotto ◽  
Dario Consonni ◽  
Lorenzo Cecconi ◽  
Dolores Catelan ◽  
Corrado Lagazio ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutant ◽  
Pollution Monitoring ◽  
Quality Data ◽  
Random Component ◽  
Deterministic Models ◽  
Prediction Uncertainty ◽  
Preferential Sampling ◽  
Sampling Locations ◽  
Concentration Surface

In this paper the focus is on environmental statistics, with the aim of estimating the concentration surface and related uncertainty of an air pollutant. We used air quality data recorded by a network of monitoring stations within a Bayesian framework to overcome difficulties in accounting for prediction uncertainty and to integrate information provided by deterministic models based on emissions meteorology and chemico-physical characteristics of the atmosphere. Several authors have proposed such integration, but all the proposed approaches rely on representativeness and completeness of existing air pollution monitoring networks. We considered the situation in which the spatial process of interest and the sampling locations are not independent. This is known in the literature as the preferential sampling problem, which if ignored in the analysis, can bias geostatistical inferences. We developed a Bayesian geostatistical model to account for preferential sampling with the main interest in statistical integration and uncertainty. We used PM10 data arising from the air quality network of the Environmental Protection Agency of Lombardy Region (Italy) and numerical outputs from the deterministic model. We specified an inhomogeneous Poisson process for the sampling locations intensities and a shared spatial random component model for the dependence between the spatial location of monitors and the pollution surface. We found greater predicted standard deviation differences in areas not properly covered by the air quality network. In conclusion, in this context inferences on prediction uncertainty may be misleading when geostatistical modelling does not take into account preferential sampling.

scholarly journals Urban PM2.5 Concentration Prediction via Attention-Based CNN–LSTM

2020 ◽  
Vol 10 (6) ◽  
pp. 1953 ◽  
Author(s):  
Songzhou Li ◽  
Gang Xie ◽  
Jinchang Ren ◽  
Lei Guo ◽  
Yunyun Yang ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Air Pollutant ◽  
Quality Data ◽  
Coefficient Of Determination ◽  
Series Data ◽  
The Past ◽  
Lstm Network ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN–LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance.

Comparison of Modeled-to-Monitored PM2.5 Exposure Concentrations Resulting from Transportation Emissions in a Near-Road Community

2020 ◽  
Vol 2674 (12) ◽  
pp. 130-143
Author(s):  
Mayra Chavez ◽  
Wen-Whai Li
Keyword(s):  
Air Quality ◽  
Meteorological Data ◽  
Temporal Variations ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Spatial And Temporal Variations ◽  
Quality Data ◽  
Background Concentrations ◽  
Regional Background ◽  
Transportation Emissions

Residents living in near-road communities are exposed to traffic-related air pollutants, which can adversely affect their health. Near-road communities are expected to observe significant spatial and temporal variations in pollutant concentrations. Determining these variations in the surrounding areas can help raise awareness among government agencies of these underserved communities living near highways. This study conducted traffic and air quality measurements along with emission and dispersion modeling of the exposure to transportation emissions of a near-road urban community adjacent to the US 54 highway (US 54), with annual average daily traffic (AADT) of 107,237. The objectives of this study were (i) to develop spatial and temporal patterns of pollutant concentration variation and (ii) to apportion the differences in exposure concentrations to background concentrations and those that are contributed from major highways. It was observed that: (a) particulate matter (PM2.5) in near-road communities is dominated by the regional background concentrations which account for more than 85% of the pollution; and (b) only near-road receptors are affected by the traffic-related air pollutant emissions from major highways while spatial and temporal variations of PM2.5 concentrations in near-road communities are less influenced by local traffic, subsiding rapidly to negligible concentrations at 300 m from the road. Modeled PM2.5 concentrations were compared with monitored data. For better air quality impact assessments, higher quality data such as time-specific traffic volume and fleet information as well as site-specific meteorological data could help yield more accurate concentration predictions. Modeled-to-monitored comparison shows that air quality in near-road communities is dominated by regional background concentrations.

Respiratory Symptoms and Spirometric Observations in Relation to Atmospheric Pollutants in a Sample of Urban Population

2000 ◽  
Vol 12 (2) ◽  
pp. 58-64 ◽  
Author(s):  
K. Satish Kumar ◽  
C.E. Prasad ◽  
N. Balakrishna ◽  
K. Visweswara Rao ◽  
P. Uma Maheswara Reddy
Keyword(s):  
Air Quality ◽  
Respiratory Symptoms ◽  
Ambient Air ◽  
Air Pollutant ◽  
Control Measures ◽  
Atmospheric Pollutants ◽  
Quality Data ◽  
Ambient Air Quality ◽  
Group I ◽  
The Mean

The prevalence of respiratory problems and the ventilatory functions in subjects belonging to three sample areas with different levels of pollution was studied to ascertain if there is any association between air pollutant levels and abnormal ventilatory functions. The predominant activity existing in that area served as the basis for stratification of the city into industrial (Group I), commercial (Group II) and residential (Group III) areas. Ambient air quality data of suspended particulate matter SPM, SO2 and NOx of the three sample areas were measured using standard methods. 216 men included in the study were administered the American Thoracic Society - Division of Lung Diseases ATS-DLD respiratory questionnaire, clinically examined and subjected to routine laboratory investigations. Spirometry and salbutamol reversibility tests were performed as per the ATS guidelines 1991. The mean and peak levels of SPM in the commercial area and the peak levels in the residential area were higher than the National Ambient Air Quality Standards (NAAQS). The mean and peak levels of NOx and SO2 in all the three areas were lower than the NAAQS. A high prevalence of ∼ 30-50% of respiratory symptoms was reported in the present study. Respiratory and ventilatory abnormalities were higher in the commercial areas, which are associated with the higher mean and peak levels of SO 2 and the peak levels of NOx. The pollution control measures should also aim at the peak levels of pollutants as they have been shown to exacerbate the respiratory symptoms in the present study. Asia Pac J Public Health 2000;12(2): 58-64

scholarly journals The influence of COVID-19 preventive measures on the air quality in Abu Dhabi (United Arab Emirates)

2021 ◽  
Author(s):  
Oriol Teixidó ◽  
Aurelio Tobías ◽  
Jordi Massagué ◽  
Ruqaya Mohamed ◽  
Rashed Ekaabi ◽  
...  
Keyword(s):  
Air Quality ◽  
United Arab Emirates ◽  
Preventive Measures ◽  
Dust Storms ◽  
Air Pollutant ◽  
Anthropogenic Sources ◽  
Abu Dhabi ◽  
Quality Data ◽  
Gaseous Pollutants ◽  
Pollutant Concentrations

AbstractThe preventive and cautionary measures taken by the UAE and Abu Dhabi governments to reduce the spread of the coronavirus disease (COVID-19) and promote social distancing have led to a reduction of mobility and a modification of economic and social activities. This paper provides statistical analysis of the air quality data monitored by the Environment Agency – Abu Dhabi (EAD) during the first 10 months of 2020, comparing the different stages of the preventive measures. Ground monitoring data is compared with satellite images and mobility indicators. The study shows a drastic decrease during lockdown in the concentration of the gaseous pollutants analysed (NO2, SO2, CO, and C6H6) that aligns with the results reported in other international cities and metropolitan areas. However, particulate matter (PM10 and PM2.5) averaged concentrations followed a markedly different trend from the gaseous pollutants, indicating a larger influence from natural events (sand and dust storms) and other anthropogenic sources. The ozone (O3) levels increased during the lockdown, showing the complexity of O3 formation. The end of lockdown led to an increase of the mobility and the air pollution; however, air pollutant concentrations remained in lower levels than during the same period of 2019. The results in this study show the large impact of human activities on the quality of air and present an opportunity for policymakers and decision-makers to design stimulus packages to overcome the economic slow-down, with strategies to accelerate the transition to resilient, low-emission economies and societies more connected to the nature that protect human health and the environment.

scholarly journals Evaluation of Ambient Air Quality Monitoring System in Jakarta: A Literatur Review

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Michelle Gunawan ◽  
Riri Asyahira ◽  
Filson M Sidjabat
Keyword(s):  
Air Quality ◽  
Monitoring System ◽  
Ambient Air ◽  
Air Pollutant ◽  
Data Availability ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Pollution Control ◽  
Air Quality Monitoring ◽  
Ambient Air Quality

<p>As the first step to air pollution control and public health protection, Air quality monitoring systems provides information that indicate the extend of pollution in an area, the source of pollution and the types of pollutants. Therefore, the aim of this study is to evaluate Jakarta’s air quality monitoring system by comparing it to the US, which participates in Indonesia’s air quality monitoring system by using their own system.  In specific, parameters such as air quality index, monitoring stations, regulation and data availability are to be compared through reviewing various literatures in detail.  The result obtained shows that the monitoring station amount is already ideal and complies to the U.S regulation. Indonesia’s ambient air quality standard need to be stricter and Air Pollutant Standard Index should include PM<sub>2.5</sub> as a parameter obtains significantly better results. Air quality data is available and accessible, although it needs to be integrated and provide real time information in a simple and effective way.</p>

scholarly journals Short-term environmental impact of ambient air quality trends in during the COVID-19 pandemic in India

2021 ◽  
Vol 5 (1) ◽  
pp. 017-025
Author(s):  
Karuppasamy Manikanda Bharath ◽  
Natesan Usha ◽  
Periyasamy Balamadeswaran ◽  
S Srinivasalu
Keyword(s):  
Air Quality ◽  
Ambient Air ◽  
Air Pollutant ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
The North ◽  
Air Quality Monitoring Stations ◽  
Air Quality Data ◽  
Monitoring Stations

The lockdown, implemented in response to the COVID-19 epidemic, restricted the operation of various sectors in the country and its highlights a good environmental outcome. Thus, a comparison of air pollutants in India before and after the imposed lockdown indicated an overall improvement air quality across major Indian cities. This was established by utilizing the Central Pollution Control Board’s database of air quality monitoring station statistics, such as air quality patterns. During the COVID-19 epidemic, India’s pre-to-post nationwide lockdown was examined. The air quality data was collected from 30-12-2019 to 28-04-2020 and synthesized using 231 Automatic air quality monitoring stations in a major Indian metropolis. Specifically, air pollutant concentrations, temperature, and relative humidity variation during COVID-19 pandemic pre-to-post lockdown variation in India were monitored. As an outcome, several cities around the country have reported improved air quality. Generally, the air quality, on a categorical scale was found to be ‘Good’. However, a few cities from the North-eastern part of India were categorized as ‘Moderate/Satisfactory’. Overall, the particulate matters reduction was in around 60% and other gaseous pollutants was in 40% reduction was observed during the lockdown period. The results of this study include an analysis of air quality data derived from continuous air quality monitoring stations from the pre-lockdown to post-lockdown period. Air quality in India improved following the national lockdown, the interpretation of trends for PM 2.5, PM 10, SO2, NO2, and the Air Quality Index has been provided in studies for major cities across India, including Delhi, Gurugram, Noida, Mumbai, Kolkata, Bengaluru, Patna, and others.

scholarly journals Evaluation of Relationship Between Air Pollutant Concentration and Meteorological Elements in Winter Months

2014 ◽  
Vol 22 (1) ◽  
pp. 25-32 ◽  
Author(s):  
Andrzej Żyromski ◽  
Małgorzata Biniak-Pieróg ◽  
Ewa Burszta-Adamiak ◽  
Zenon Zamiar
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Winter Season ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Meteorological Elements

Abstract The paper presents the evaluation of the relation between meteorological elements and air pollutants’ concentrations. The analysis includes daily concentrations of pollutants and variation of meteorological elements such as wind speed, air temperature and relative humidity, precipitation and total radiation at four monitoring stations located in the province of Lower Silesia in individual months of the winter half-year (November–April, according to hydrological year classification) of 2005–2009. Data on air quality and meteorological elements came from the results of research conducted in the automatic net of air pollution monitoring conducted in the range of the State Environment Monitoring. The effect of meteorological elements on analysed pollutant concentration was determined using the correlation and regression analysis at significance level α < 0.05. The occurrence of maximum concentration of NO, NO2, NOX, SO2 and PM10 occurred in the coldest months during winter season (January, February and December) confirmed the strong influence of “low emission” on air quality. Among the meteorological factors assessed wind speed was most often selected component in step wise regression procedure, then air temperature, less air relative humidity and solar radiation. In the case of a larger number of variables describing the pollution in the atmosphere, in all analyzed winter seasons the most common set of meteorological elements were wind speed and air temperature.

scholarly journals An Air Quality Data Analysis System for Interrelating Effects, Standards, and Needed Source Reductions: Part 12. Effects on Man, Animals, and Plants as a Function of Air Pollutant Impact

Air & Waste ◽  
1993 ◽  
Vol 43 (12) ◽  
pp. 1585-1592 ◽  
Author(s):  
Ralph I. Larsen ◽  
William F. McDonnell ◽  
David L. Coffin ◽  
Walter W. Heck
Keyword(s):  
Data Analysis ◽  
Air Quality ◽  
Air Pollutant ◽  
Quality Data ◽  
Analysis System ◽  
Data Analysis System ◽  
Air Quality Data

scholarly journals Short-Term Associations between Morbidity and Air Pollution in Metropolitan Area of Monterrey, Mexico

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1352
Author(s):  
Rosa Maria Cerón Breton ◽  
Julia Céron Breton ◽  
María de la Luz Espinosa Fuentes ◽  
Jonathan Kahl ◽  
Alberto Antonio Espinosa Guzman ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Metropolitan Area ◽  
Hospital Admissions ◽  
Risk Index ◽  
Air Pollutant ◽  
Quality Data ◽  
Short Term ◽  
Pollution Effects ◽  
National System

Short-term effects of air pollution on the number of hospital admissions in eight municipalities of the Metropolitan Area of Monterrey, Mexico, were assessed from 2016 to 2019 using a time-series approach. Air quality data were obtained from the Atmospheric Monitoring System of Nuevo Leon State (SIMA) which belongs to SINAICA (National System of Air Quality Information), providing validated data for this study. Epidemiological data were provided by SINAIS (National System of Health Information), considering admission by all causes and specific causes, gender and different age groups. Guadalupe had the highest mean concentrations for SO2, CO and O3; whereas Santa Catarina showed the highest NO2 concentrations. Escobedo and Garcia registered the highest levels for PM10. Only PM10 and O3 exceeded the permissible maximum values established in Mexican official standards. A basal Poisson model was constructed to assess the association between daily morbidity and air pollutants, from this, a second scenario in which daily mean concentrations of air pollutant criteria increase by 10% was considered. Most of pollutants and municipalities studied showed a great number of associations between an increase of 10% in their current concentrations and morbidity, especially for the age group between 5 and 59 years during cold months, excepting ozone which showed a strongest correlation during summer. Results were comparable to those reported by other authors around the world, however, in spite of relative risk index (RRI) values being low, they are of public concern. This study demonstrated that considering the nature of their activities, economically active population and students, they could be more vulnerable to air pollution effects. Results found in this study can be used by decision makers to develop public policies focused on protecting this specific group of the population in metropolitan areas in Mexico.

scholarly journals WEB BASED 3D VISUALISATION OF TIME-VARYING AIR QUALITY INFORMATION

2018 ◽  
Vol XLII-4 ◽  
pp. 267-274
Author(s):  
U. Isikdag ◽  
K. Sahin
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Air Pollutant ◽  
Quality Data ◽  
Interactive System ◽  
Quality Information ◽  
Time Varying ◽  
Web Based ◽  
3D Visualisation ◽  
Usability Tests

<p><strong>Abstract.</strong> Many countries where the industrial development and production rates are high face many side effects of low air quality and air pollution. There is an evident correlation between the topographic and climatic properties of a location and the air pollution and air quality on that location. As the variation of air quality is dependent on location, air quality information should be acquired, utilised, stored and presented in form of Geo-Information. On the other hand, as this information is related with the health concerns of public, the information should be available publicly, and needs to be presented through an easily accessible medium and through a commonly used interface. Efficient storage of time-varying air quality information when combined with an efficient mechanism of 3D web-based visualisation would help very much in dissemination of air quality information to public. This research is focused on web-based 3D visualisation of time-varying air quality data. A web based interactive system is developed to visualise pollutant levels that were acquired as hourly intervals from more than 100 stations in Turkey between years 2008 and 2017. The research also concentrated on visualisation of geospatial high volume data. In the system, visualisation can be achieved on-demand by querying an air pollutant information database of 10.330.629 records and a city object database with more than 700.000 records. The paper elaborates on the details of this research. Following a background on air quality, air quality models, and Geo-Information visualisation, the system architecture and functionality is presented. The paper concludes with results of usability tests of the system.</p>

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