Building spatial-temporal NO2 land use regression models in complex urban environment

2020 ◽  
Author(s):  
Peng Wei ◽  
Yang Xing ◽  
Li Sun ◽  
Zhi Ning
Keyword(s):  
Land Use ◽  
Hong Kong ◽  
Air Quality ◽  
Urban Environment ◽  
Real Time ◽  
Urban Areas ◽  
Personal Exposure ◽  
Pollutant Emission ◽  
Land Use Regression ◽  
Pollutant Distribution

<p>Air quality and traffic-related pollutants in urban areas are major concerns especially in meg-cities. Current Air Quality Monitoring Station (AQMS) cannot sufficiently reveal these pollution conditions with limited point measurements and limited information cannot supply adequate insight on personal exposure in a complex urban environment. Land Use Regression (LUR) model provided a feasible solution for estimating outdoor personal exposure by adding multiple data sources. However, fixed-site passive monitoring still lacks enough spatial coverage or spatial flexibility to estimate pollutant distribution at the fine-scale level.</p><p>A Mobile Air Sensor Network (MASEN) project was deployed in the Hong Kong area, with electrochemical gas sensors installed on the routine buses to capture on-road NO<sub>x</sub> pollutant measurement, the data was collected by the integrated sensor system and transfer to the database for real-time visualization. Compared with previous mobile measurements used for LUR model building which limited to 1-2 routes, this measurement covered major roads in the Hong Kong area and get an overview of pollutant distribution at various ambient. Two main variables were introduced to improve the model performance: 1) Sky View Factor (SVF) which represented pollutant dispersion status were obtained from Google street view image, a deep learning model was used for scene parsing to recognized targets in this procedure, 2) a Real-time Traffic Congestion Index (RTCI) which represented traffic pollutants emission was obtained from Google map and merged with road network. A common LUR model will be built based on a distance-decay regression selection strategy for variables selection. Meanwhile, a spatial-temporal LUR model will be built which contained both diurnal variability and day-to-day variability. Finally, a high-resolution pollution map of the urban areas will illustrate NO<sub>2</sub> pollutant distribution.</p><p>In this work, we aimed at estimating traffic-related pollutants in a complex city environment and identifying hotspots at both spatial and temporal aspects. Meanwhile, the novel data source which closely associated with traffic-related pollutant emission also gives a better understanding of guidance on urban planning.</p>

Improving the Protection of Children Against Air Pollution Threats in Romania � The RokidAIR Project Approach and Future Perspectives

2017 ◽  
Vol 68 (4) ◽  
pp. 841-846
Author(s):  
Hai-Ying Liu ◽  
Daniel Dunea ◽  
Mihaela Oprea ◽  
Tom Savu ◽  
Stefania Iordache
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Health Effects ◽  
Urban Areas ◽  
Behavioural Change ◽  
Health Assessment ◽  
Warning System ◽  
Personal Exposure ◽  
Mobile Technologies ◽  
Quality Maps

This paper presents the approach used to develop the information chain required to reach the objectives of the EEA Grants� RokidAIR project in two Romanian cities i.e., Targoviste and Ploiesti. It describes the PM2.5 monitoring infrastructure and architecture to the web-based GIS platform, the early warning system and the decision support system, and finally, the linking of air pollution to health effects in children. In addition, it shows the analysis performance of the designed system to process the collected time series from various data sources using the benzene concentrations monitored in Ploiesti. Moreover, this paper suggests that biomarkers, mobile technologies, and Citizens� Observatories are potential perspectives to improve data coverage by the provision of near-real-time air quality maps, and provide personal exposure and health assessment results, enabling the citizens� engagement and behavioural change. This paper also addresses new fields in nature-based solutions to improve air quality, and studies on air pollution and its mental health effects in the urban areas of Romania.

scholarly journals Development of Land Use Regression Model for Seasonal Variation of Nitrogen Dioxide (NO2) in Lahore, Pakistan

2021 ◽  
Vol 13 (9) ◽  
pp. 4933
Author(s):  
Saimar Pervez ◽  
Ryuta Maruyama ◽  
Ayesha Riaz ◽  
Satoshi Nakai
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Model ◽  
Urban Areas ◽  
Industrial Development ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Land Use Classification ◽  
Land Use Regression ◽  
Air Exposure

Ambient air pollution and its exposure has been a worldwide issue and can increase the possibility of health risks especially in urban areas of developing countries having the mixture of different air pollution sources. With the increase in population, industrial development and economic prosperity, air pollution is one of the biggest concerns in Pakistan after the occurrence of recent smog episodes. The purpose of this study was to develop a land use regression (LUR) model to provide a better understanding of air exposure and to depict the spatial patterns of air pollutants within the city. Land use regression model was developed for Lahore city, Pakistan using the average seasonal concentration of NO2 and considering 22 potential predictor variables including road network, land use classification and local specific variable. Adjusted explained variance of the LUR models was highest for post-monsoon (77%), followed by monsoon (71%) and was lowest for pre-monsoon (70%). This is the first study conducted in Pakistan to explore the applicability of LUR model and hence will offer the application in other cities. The results of this study would also provide help in promoting epidemiological research in future.

scholarly journals PARAMETERS OF INTEREST FOR THE DESIGN OF GREEN INFRASTRUCTURE

2019 ◽  
pp. 92-101
Author(s):  
Marija Šperac ◽  
Dino Obradović
Keyword(s):  
Air Quality ◽  
Urban Environment ◽  
Air Temperature ◽  
Green Infrastructure ◽  
Land Surface ◽  
Urban Areas ◽  
Hydrological Cycle ◽  
Urban Environments ◽  
Land Surfaces ◽  
Selection Of

The urbanization process significantly reduced the permeability of land surfaces, which affected the changes of runoff characteristics and the relations in the hydrological cycle. In urban environments, the relationships within the hydrological cycle have changed in quantity, in particular: precipitation, air temperature, evaporation, and infiltration. By applying the green infrastructure (GI) to urban environments is beneficial for the water resources and the social community. GI has an effect on the improvement of ecological, economic, and social conditions. Using GI into urban areas increases the permeability of land surfaces, whereby decreasing surface runoff, and thus the frequency of urban floods. It also has a significant influence on the regulation of air quality, water purification, climate change impact, and the changes in the appearance of the urban environment. When planning and designing the GI, it is necessary to identify the type of GI and determine the size and location of the selected GI. Since each urban environment has its own characteristics, it is necessary to analyze them before deciding on the GI. The paper analyzed meteorological parameters (precipitation, air temperature, insolation, air humidity) affecting the selection of GI types, using the specific example of an urban environment – the City of Osijek, Croatia. Significant parameters when designing GI are operation and maintenance These parameters directly affect the efficiency of GI. The proper selection of GI and its location results in maximum gains: the reduction of land surface drainage - drainage of the sewage system, purification and retention of precipitation at the place of production, the improvement of air quality, and the improvement of living conditions in urban environments

scholarly journals Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda

2020 ◽  
Author(s):  
Eric S. Coker ◽  
Ssematimba Joel ◽  
Engineer Bainomugisha
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Low Cost ◽  
Additive Models ◽  
Pollution Monitoring ◽  
Sub Saharan Africa ◽  
Land Use Regression ◽  
Sub Saharan

Background: There are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods: Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results: Monthly PM2.5 concentration was 60.2 µg/m3 (IQR: 45.4-73.0 µg/m3; median= 57.5 µg/m3). For the ML LUR models, RMSE values ranged between 5.43 µg/m3 - 15.43 µg/m3 and explained between 28% and 92% of monthly PM2.5 variability. Generalized additive models explained the largest amount of PM2.5 variability (R2=0.92) and produced the lowest RMSE (5.43 µg/m3) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion: To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.

scholarly journals Use of Low-cost Sensors to Develop Land Use Regression Models for PM2.5 of 6 Urban Areas in the US

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
T. Lu ◽  
M.J. Bechle ◽  
Y. Wan ◽  
A.A. Presto ◽  
S. Hankey
Keyword(s):  
Land Use ◽  
Urban Areas ◽  
Regression Models ◽  
Low Cost ◽  
Land Use Regression ◽  
The Us ◽  
Develop Land

scholarly journals Integrating travel behavior with land use regression to estimate dynamic air pollution exposure in Hong Kong

2018 ◽  
Vol 113 ◽  
pp. 100-108 ◽  
Author(s):  
Robert Tang ◽  
Linwei Tian ◽  
Thuan-Quoc Thach ◽  
Tsz Him Tsui ◽  
Michael Brauer ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Hong Kong ◽  
Travel Behavior ◽  
Land Use Regression ◽  
Air Pollution Exposure ◽  
Pollution Exposure

scholarly journals Estimating daily surface NO<sub>2</sub> concentrations from satellite data - A case study over Hong Kong using land use regression models

2016 ◽  
Author(s):  
Jasdeep S Anand ◽  
Paul S Monks
Keyword(s):  
Land Use ◽  
Hong Kong ◽  
Satellite Data ◽  
Monitoring Network ◽  
Fine Scale ◽  
Land Use Regression ◽  
Vertical Column ◽  
Mixed Effect ◽  
In Situ Data

Abstract. Land Use Regression (LUR) models have been used in epidemiology to determine the fine-scale spatial variation in air pollutants such as nitrogen dioxide (NO2) in cities and larger regions. However, they are often limited in their temporal resolution, which may potentially be rectified by employing the synoptic coverage provided by satellite measurements. In this work a mixed effects LUR model is developed to model daily surface NO2 concentrations over the Hong Kong SAR during 2005-2015. In-situ measurements from the Hong Kong Air Quality Monitoring Network, along with tropospheric vertical column density (VCD) data from the OMI, GOME-2A and SCIAMACHY satellite instruments were combined with fine-scale land use parameters to provide the spatiotemporal information necessary to predict daily surface concentrations. Cross-validation with the in-situ data shows that the mixed effect LUR model using OMI data has a high predictive power (adj. R2 = 0.84), especially when compared with surface concentrations derived using the MACC-II reanalysis model dataset (adj. R2 = 0.11). Time series analysis shows no statistically significant trend in NO2 concentrations during 2005-2015, despite a reported decline in NOx emissions. This study demonstrates the utility in combining satellite data with LUR models to derive daily maps of ambient surface NO2 for use in exposure studies.

scholarly journals Exploring the Spatial and Temporal Relationship between Air Quality and Urban Land-Use Patterns Based on an Integrated Method

2020 ◽  
Vol 12 (7) ◽  
pp. 2964 ◽  
Author(s):  
Chia-An Ku
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Urban Areas ◽  
Temporal Relationship ◽  
Urban Land ◽  
Urban Land Use ◽  
Air Pollutant ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Mitigation Planning

The deterioration of air quality in urban areas is often closely related to urbanization, as this has led to a significant increase in energy consumption and the massive emission of air pollutants, thereby exacerbating the current state of air pollution. However, the relationship between urban development and air quality is complex, thus making it difficult to be analyzed using traditional methods. In this paper, a framework integrating spatial analysis and statistical methods (based on 170 regression models) is developed to explore the spatial and temporal relationship between urban land use patterns and air quality, aiming to provide solid information for mitigation planning. The thresholds for the influence of urban patterns are examined using different buffer zones. In addition, the differences in the effects of various types of land use pattern on air quality were also explored. The results show that there were significant differences between 1999 and 2013 with regards to the correlations between land use patterns and air pollutant concentrations. Among all land uses, forest, water and built-up areas were proved to influence concentrations the most. It is suggested that the developed framework should be applied further in the real-world mitigation planning decision-making process

scholarly journals A tale of four cities: A semantic analysis comparing the newspaper coverage of air pollution in Hong Kong, London, Pittsburgh, and Tianjin from 2014 to 2017

2019 ◽  
Vol 41 (1) ◽  
pp. 37-52
Author(s):  
Tongxin Sun ◽  
Bu Zhong
Keyword(s):  
Air Pollution ◽  
Hong Kong ◽  
Air Quality ◽  
Real Time ◽  
Semantic Analysis ◽  
Newspaper Coverage ◽  
Word Count ◽  
The Public ◽  
Computer Aided ◽  
Linguistic Inquiry

A computer-aided semantic analysis (using Linguistic Inquiry and Word Count [LIWC]) examined how newspaper coverage of air pollution from 2014 to 2017 may affect the public agenda in four cities—Hong Kong, London, Pittsburgh, and Tianjin. Results show that after controlling for the real-time air quality, the agenda-setting effect was found in Hong Kong, London, and Pittsburgh, but not Tianjin. Tianjin’s reports also contained more future-framed words but fewer present-framed words than other cities.

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