Forecasting UK site level air quality with a Kalman filtering approach

2021 ◽  
Author(s):  
Rachael Duncan ◽  
Paul Young ◽  
Chris Nemeth
Keyword(s):  
Air Quality ◽  
Statistical Model ◽  
Kalman Filtering ◽  
Kalman Filters ◽  
Process Model ◽  
Ambient Air ◽  
Pollutant Emissions ◽  
Quality Data ◽  
Measurement Information ◽  
The Uk

<p>Despite efforts to reduce pollutant emissions in the UK, between 28,000 and 36,000 deaths a year are attributable to poor air quality and ambient air pollution is considered the UK’s biggest environmental threat to health. Characterising, quantifying and understanding air quality variability and the importance of different drivers is essential to guide policies to address the issue and its risks, for both the short and long term. Here we investigate a statistical modelling approach to characterise air quality variability and its key drivers, using Kalman filters. Kalman filters are a commonly used tool in air quality modelling but are seldom used in a statistic framework that accounts for uncertainty in a principled way. Kalman filtering allows us to take data which is noisy or partially recorded, such as air quality data, and help reveal the true underlying trends and dynamics of the data. This allows us to combine measurement information with the statistical model to obtain an air quality forecast, using the measurement information to reduce the statistical model errors and improve model results. We explore this approach using air quality monitoring data from the UK Automatic Urban and Rural Network (AURN), which consists of 150 sites focussed mainly in populated areas, leaving large areas unmonitored. AURN is primarily used for compliance reporting against national and European air quality standards and targets. Eventually, our aim is to provide short-term forecasts of pollutant levels from AURN, comparing this against process model forecasts and ultimately providing an optimised combination of process model, statistical model and measurement.   </p>

scholarly journals Spatio-temporal of surface ozone (O3) variations at urban and suburban sites in Sarawak region of Malaysia

2021 ◽  
Vol 880 (1) ◽  
pp. 012004
Author(s):  
H Mahidin ◽  
M T Latif ◽  
A Hamdan ◽  
J Salleh ◽  
D Dominick ◽  
...  
Keyword(s):  
Air Quality ◽  
Surface Ozone ◽  
Ambient Air ◽  
Quality Standard ◽  
Transportation Systems ◽  
Pollutant Emissions ◽  
Daily Maximum ◽  
Spatio Temporal ◽  
Ambient Air Quality Standard ◽  
The Relationship

Abstract Sarawak Region of Malaysia is currently experiencing a high demand for capital needs such as transformation forest to plantations, economic development, and improving transportation systems. Those land cover changes will increase primary pollutant emissions and trigger surface O3 formation. Surface O3 is a secondary pollutant and a significant greenhouse gas contributing to climate change and declining air quality. In this study, variations in surface O3 concentrations at urban and suburban sites in Sarawak were explored using the Malaysian Department of Environment data spanning a two-year cycle (2018-2019). The primary aim of this study is to ascertain the variation of surface O3 concentrations reported at four monitoring stations in Sarawak, namely Kuching (SQ1) (Urban), Sibu (SQ2) (Suburban), Bintulu (SQ3) (Suburban), and Miri (SQ4) (Suburban). The study also analysed the relationship between O3 distribution and nitrogen oxides (NO and NO2). The findings showed that O3 concentrations observed in the region during the study period were lower than the maximum permissible value of 100 ppbv suggested by the Malaysian Ambient Air Quality Standard (2020). SQ4 (Miri) at suburban sites recorded the highest average surface O3 concentrations with an hourly average and daily maximum O3 concentration of 15.7 and 89.5 ppbv, respectively. Temperatures, UV exposure, and wind speed all impact the concentration of surface O3 in Sarawak. In all stations, concentrations of O3 were inversely linked with NO, NO2, and relative humidity (RH). This research will assist the relevant agency in forecast, monitor, and mitigate the level of O3 in the ambient environment, especially in the Sarawak Region.

A PRELIMINARY SURVEY OF AIR QUALITY IN MAKASSAR CITY SOUTH SULAWESI INDONESIA

2012 ◽  
Vol 57 (1) ◽  
Author(s):  
SATTAR A. ◽  
M. RASHID ◽  
R. MAT ◽  
L. PUJI
Keyword(s):  
Air Quality ◽  
Suspended Particle ◽  
Ambient Air ◽  
Quality Standard ◽  
World Health ◽  
Quality Data ◽  
Preliminary Survey ◽  
South Sulawesi ◽  
Ambient Air Quality Standard ◽  
Health Organization

Makassar has a strategic position as it is located in between the south and north in the provinces of South Sulawesi. Thus, the rapid growth of urbanization and industrialization within the area is unavoidable, resulting Makassar to be an area of mixed commercial–residential–industrial along with the problem of air pollution. Hence, it is important to monitor the quality of air in Makassar. This paper presents a preliminary survey of urban air quality in Makassar area based on SO2, CO, NO2, O3, Pb, and TSP (Total Suspended Particle) sampled over ten years period (2001 to 2010), while PM10was monitored for five years (2006 to 2010). The air quality data were obtained from measurements made by the Office of Ministry of Environment Sulawesi, Maluku and Papua and Environment Board of the Province of South Sulawesi as well as Environment agency of Makassar City. The average annual concentrations of SO2, CO, NO2, O3, Pb, TSP and PM10 recorded were 76 μg/m3, 1041 μg/m3, 43.2 μg/m3, 54.5 μg/m3, 0.7 μg/m3, 188 μg/m3, 54.6 μg/m3, respectively. Subsequently, these data are compared to the air quality threshold limits recommended by the Indonesia National Ambient Air Quality Standard (INAAQS) as well as guidelines of the World Health Organization (WHO).

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.

scholarly journals Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique

2019 ◽  
Vol 19 (17) ◽  
pp. 11303-11314 ◽  
Author(s):  
Tuan V. Vu ◽  
Zongbo Shi ◽  
Jing Cheng ◽  
Qiang Zhang ◽  
Kebin He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Action Plan ◽  
Ambient Air ◽  
Meteorological Conditions ◽  
Assessment Process ◽  
Pollutant Emissions ◽  
Ambient Air Quality ◽  
Clean Air ◽  
The Impact

Abstract. A 5-year Clean Air Action Plan was implemented in 2013 to reduce air pollutant emissions and improve ambient air quality in Beijing. Assessment of this action plan is an essential part of the decision-making process to review its efficacy and to develop new policies. Both statistical and chemical transport modelling have been previously applied to assess the efficacy of this action plan. However, inherent uncertainties in these methods mean that new and independent methods are required to support the assessment process. Here, we applied a machine-learning-based random forest technique to quantify the effectiveness of Beijing's action plan by decoupling the impact of meteorology on ambient air quality. Our results demonstrate that meteorological conditions have an important impact on the year-to-year variations in ambient air quality. Further analyses show that the PM2.5 mass concentration would have broken the target of the plan (2017 annual PM2.5<60 µg m−3) were it not for the meteorological conditions in winter 2017 favouring the dispersion of air pollutants. However, over the whole period (2013–2017), the primary emission controls required by the action plan have led to significant reductions in PM2.5, PM10, NO2, SO2, and CO from 2013 to 2017 of approximately 34 %, 24 %, 17 %, 68 %, and 33 %, respectively, after meteorological correction. The marked decrease in PM2.5 and SO2 is largely attributable to a reduction in coal combustion. Our results indicate that the action plan has been highly effective in reducing the primary pollution emissions and improving air quality in Beijing. The action plan offers a successful example for developing air quality policies in other regions of China and other developing countries.

scholarly journals Research on Air Quality Analysis Based on Cluster Methods

2021 ◽  
Vol 898 (1) ◽  
pp. 012024
Author(s):  
Zhaoni Li ◽  
Jian Zheng
Keyword(s):  
Air Quality ◽  
Ambient Air ◽  
Quality Analysis ◽  
Quality Data ◽  
Ambient Air Quality ◽  
Republic Of China ◽  
Pollution Levels ◽  
Analysis Process ◽  
Cluster Methods ◽  
The Impact

Abstract Research on air quality analysis is a hot field. Here we describe an analysis process based on cluster methods for the data of ambient air quality. In this paper, we use the process to cluster on the air quality data which from the National Urban Air Quality Report in December 2020 on the official website of the Ministry of Ecology and Environment of the People’s Republic of China. We find that cities in different clusters with different main pollutants and pollution levels. Ambient air quality analysis aims to provide guidance for reducing the impact of air pollution on health.

scholarly journals Ambient air quality data reported at Sasol Secunda monitoring stations during COVID-19 lockdown – Mpumalanga, South Africa

2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Andrew Venter ◽  
Sandra De Vos
Keyword(s):  
South Africa ◽  
Air Quality ◽  
Ambient Air ◽  
Human Behaviour ◽  
Quality Data ◽  
Ambient Air Quality ◽  
Industrial Facilities ◽  
Ambient Temperatures ◽  
Pollutant Concentrations ◽  
Monitoring Stations

Various local and international research has been published on the effects of COVID-19 lockdown on ambient air quality. In most cases, a reduction in ambient NOx and PM concentrations have been observed with varying changes in ambient SO2 levels. Secunda, located in the Highveld Priority Area in Mpumalanga, South Africa is known for its large industrial facilities utilising coal as primary feedstock. The towns of Secunda and eMbalenhle provide the majority of the workforce to Sasol and has therefore been the focus of this study. The ambient air quality in the Secunda region was assessed due to the changes in human behaviour during lockdown, familiarity with the Sasol facility and the strategic locations of ambient air quality stations.Results show a clear decrease in ambient CO, NO2 and PM concentrations, especially during the first two weeks of lockdown. Only subtle changes were observed for ambient H2S and SO2 pollutant concentrations at the ambient monitoring stations. An increasing trend in all ambient species was observed towards the end and post lockdown, in contrast to declining ambient temperatures with the onset of winter. This is also contrary to the reduction in emissions from the factory that conducted annual maintenance in the month following lockdown (phase shutdown). This article concludes that human behaviour has a material local ambient impact on CO, NO2 and PM pollutant species, while H2S concentration profiles are more directly related to the industrial complex’s levels of activity. Ambient SO2 trends did not show a similar correlation with the facility’s activities (as H2S), but a stronger correlation was observed with the diverse local and regional sources in close proximity to Secunda and eMbalenhle. The influence of better dispersion especially on a local scale, brought about by more effective emission heights, is considered material. Moreover, meteorological factors, on local air quality, has been shown to be a material contributor to observed ambient air quality levels in the study domain

scholarly journals Air quality health indices - review

2018 ◽  
Vol 247 ◽  
pp. 00002 ◽  
Author(s):  
Anna Gayer ◽  
Łukasz Adamkiewicz ◽  
Dominika Mucha ◽  
Artur Badyda
Keyword(s):  
Air Quality ◽  
Hospital Admissions ◽  
Environmental Epidemiology ◽  
Ambient Air ◽  
Quality Data ◽  
Health Indices ◽  
Quality Health ◽  
On Line ◽  
Air Quality Health Index ◽  
The Impact

Many studies have shown associations between exposure to air pollutants and negative health effects such as increased number of Hospital Admissions for respiratory and cardiovascular diseases or even increased daily mortality due to those causes. To assess air quality in ambient air continuous monitoring is run in many cities worldwide. Data which is collected at these points should represent exposure of the population and is used to monitor medium and long-term trends. To provide an information for citizens about the impact of air quality on their health several governmental and municipal agencies developed air quality health indices These tools are based on environmental epidemiology models and on-line air quality data. The health risk is assessed differently for each index. In this paper review of Canadian, American, Hong Kong’s Air Quality Health Index.

scholarly journals Land Use and Ambient Air Quality in Bahir Dar and Hawassa, Ethiopia

2018 ◽  
Vol 11 ◽  
pp. 117862211775213 ◽  
Author(s):  
Oluwasinaayomi Faith Kasim ◽  
Muluneh Woldetisadik Abshare ◽  
Truphena Eshibukule Mukuna ◽  
Bolanle Wahab
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Ambient Air ◽  
Urban Land Use ◽  
Quality Data ◽  
Bahir Dar ◽  
Ambient Air Quality ◽  
Industrial Land Use ◽  
Residential Land Use ◽  
Mean Concentration

Land use, air pollution, and climate change are closely related. This article analysed the contributions of urban land use to ambient air quality in Bahir Dar and Hawassa cities. A total of 32 geo-referenced locations, 16 each in Bahir Dar and Hawassa, representing different land uses, were assessed for carbon monoxide (CO), carbon dioxide (CO2), and volatile organic compound (VOC). CO2 concentration (ppm) for Bahir Dar and Hawassa ranged from 385.10 ± 15.34 ppm (recreational land use) to 555.50 ± 80.79 ppm (commercial land use) and 388.07 ± 19.79 ppm (recreational land use) to 444.50 ± 54.05 ppm (industrial land use), respectively, whereas mean concentration of CO was 0.01 ± 0.01 ppm (recreational land use) to 2.59 ± 0.69 ppm (circulation land use) and 0.12 ± 0.11 ppm (recreational land use) to 4.66 ± 1.41 ppm (circulation land use), respectively. The VOC values were 882.10 ± 147.05 ppm (residential land use) to 1436.00 ± 932.06 ppm (institutional land use) and 1377.30 ± 233.23 ppm (institutional land use) to 2132.33 ± 739.71 ppm (circulation land use). Inadequate monitoring, occasioned by dearth of equipment, poor urban management strategy, fossil fuel combustion, and aged vehicles were some of the factors responsible for the observed concentrations. Elevated levels of CO, CO2, and VOC in the atmosphere have a significant impact on global warming, with adverse effects on human health. Capacity for monitoring, analysis, reporting, and validation of air quality data in the cities should be strengthened.

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