A data fusion method to improve winter PM10 concentration predictions in Budapest based on the CAMS air quality models

2020 ◽  
Author(s):  
Adrienn Varga-Balogh ◽  
Ádám Leelőssy ◽  
István Lagzi ◽  
Róbert Mészáros
Keyword(s):  
Air Quality ◽  
Data Fusion ◽  
Real Time ◽  
Large Scale ◽  
Urban Traffic ◽  
Pm10 Concentration ◽  
Fusion Method ◽  
Concentration Data ◽  
Model Predictions ◽  
Urban Monitoring

<div> <p><span>Winter air pollution in Budapest is a major environmental issue, caused by an interaction of residential heating, urban traffic and large-scale transport. I</span><span>ncreasing public and political demand are</span><span> present to achieve </span><span>more accurate air quality predictions to support both real-time public health measures and long-term mitigation policies.  </span><span>A</span><span>tmospheric chemistry and transport models of the Copernicus Atmospheric Monitoring Service (CAMS) provide </span><span>near-real-time </span><span>air quality forecasts for Europe</span><span>. The validation of these model predictions for Budapest showed that although large-scale processes are well captured, the complex interaction of large-scale plumes with significant and highly variable local residential emissions leads to the underestimation of winter PM10 concentrations. Furthermore, CAMS models are not expected to fully predict the non-representative concentrations at specific urban monitoring locations, which, on the other hand, serve as the legal basis of all public policies and measures. Therefore, obtaining a relationship between monitoring site observations and CAMS model predictions is of primary importance.</span> </p> </div><div> <p><span>In this study, we used observed PM10 concentration data from 12 air quality monitoring sites within Budapest, as well as 24-hour predictions from 7 of the 9 CAMS models to </span><span>produce an optimal linear combination of models that best matched, in terms of RMSE, the observed time series. A zero-degree term to correct the model bias was also applied. The applied data fusion method was cross-validated on urban monitoring sites not used in fitting the model, and found to improve PM10 forecast validation statistics compared to the pointwise model median (CAMS ensemble) as well as each of the 7 single models. The presented fusion of CAMS models can therefore provide an improved prediction of PM10 concentrations at urban monitoring sites in Budapest. </span> </p> </div>

scholarly journals Can the New Subway Line Openings Mitigate PM10 Concentration? Evidence from Chinese Cities Based on the PSM-DID Method

2020 ◽  
Vol 17 (13) ◽  
pp. 4638
Author(s):  
Ying Wang ◽  
Jing Tao ◽  
Rong Wang ◽  
Chuanmin Mi
Keyword(s):  
Air Quality ◽  
Treatment Effect ◽  
Large Scale ◽  
Time Windows ◽  
Pm10 Concentration ◽  
Mechanism Analysis ◽  
Short Term ◽  
Concentration Data ◽  
Short Term Treatment ◽  
The Impact

The large-scale construction of subway systems, which is viewed as one of the potential measures to mitigate traffic congestion and its resulting air pollution and health impact, is taking place in major cities throughout China. However, the literature on the impact of the new subway line openings on particulate matter with a diameter less than 10 µm (PM10) at the city level is scarce. Employing the Propensity Score Matching–Difference-in-differences method, this paper examines the effect of the new subway line openings on air quality in terms of PM10 in China, using the daily PM10 concentration data from January 2014 to December 2017. Our finding shows that the short-term treatment effect on PM10 is more controversial. Furthermore, for different time windows, the result confirms an increase in PM10 pollution during the short term, while the subway line openings improve air quality in the longer term. In addition, we find that the treatment effect results in high PM10 pollution for cities with 1–2 million people, while it improves air quality for cities with over 2 million people. Moreover, for cities with varying levels of GDP, there is evidence of a reduction in PM10 after the subway line openings. Mechanism analysis supports the conclusion that the PM10 reduction originated from substituting the subway for driving.

scholarly journals Real-Time Probabilistic Data Fusion for Large-Scale IoT Applications

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 10015-10027 ◽  
Author(s):  
Adnan Akbar ◽  
George Kousiouris ◽  
Haris Pervaiz ◽  
Juan Sancho ◽  
Paula Ta-Shma ◽  
...  
Keyword(s):  
Data Fusion ◽  
Real Time ◽  
Large Scale ◽  
Probabilistic Data ◽  
Iot Applications

scholarly journals Evaluation of Boosted Regression Tree for the Prediction of the Maximum 24-Hour Concentration of Particulate Matter

2021 ◽  
Vol 12 (4) ◽  
pp. 126-130
Author(s):  
Wan Nur Shaziayani ◽  
◽  
Ahmad Zia Ul-Saufie ◽  
Syarifah Adilah Mohamed Yusoff ◽  
Hasfazilah Ahmat ◽  
...  
Keyword(s):  
Air Quality ◽  
Regression Tree ◽  
Pm10 Concentration ◽  
Boosted Regression Trees ◽  
Concentration Data ◽  
Boosted Regression Tree ◽  
Independent Test ◽  
Multiple Regression Models ◽  
Daily Pm10 ◽  
Fold Cross Validation

Air pollution is a considerable health danger to the environment. The objective of this study was to assess the characteristics of air quality and predict PM10 concentrations using boosted regression trees (BRTs). The maximum daily PM10 concentration data from 2002 to 2016 were obtained from the air quality monitoring station in Kuching, Sarawak. Eighty percent of the monitoring records were used for the training and twenty percent for the validation of the models. The best iteration of the BRT model was performed by optimizing the prediction performance, while the BRT algorithm model was constructed from multiple regression models. The two main parameters that were used were the learning rate (lr) and tree complexity (tc), which were fixed at 0.01 and 5, respectively. Meanwhile, the number of trees (nt) was determined by using an independent test set (test), a 5-fold cross validation (CV) and out-of-bag (OOB) estimation. The algorithm model for the BRT produced by using the CV was the best guide to be used compared with the OOB to test the predicted PM10 concentration. The performance indicators showed that the model was adequate for the next day’s prediction (PA=0.638, R2=0.427, IA=0.749, NAE=0.267, and RMSE=28.455).

scholarly journals Real-Time Predictive Control Strategy Optimization

2020 ◽  
Vol 2674 (3) ◽  
pp. 1-11 ◽  
Author(s):  
Samarth Gupta ◽  
Ravi Seshadri ◽  
Bilge Atasoy ◽  
A. Arun Prakash ◽  
Francisco Pereira ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Large Scale ◽  
Dynamic Traffic Assignment ◽  
Control Strategies ◽  
Urban Traffic ◽  
Computational Performance ◽  
Management Measures ◽  
Network States ◽  
Traffic Guidance

Urban traffic congestion has led to an increasing emphasis on management measures for more efficient utilization of existing infrastructure. In this context, this paper proposes a novel framework that integrates real-time optimization of control strategies (tolls, ramp metering rates, etc.) with the generation of traffic guidance information using predicted network states for dynamic traffic assignment systems. The efficacy of the framework is demonstrated through a fixed demand dynamic toll optimization problem, which is formulated as a non-linear program to minimize predicted network travel times. A scalable efficient genetic algorithm that exploits parallel computing is applied to solve this problem. Experiments using a closed-loop approach are conducted on a large-scale road network in Singapore to investigate the performance of the proposed methodology. The results indicate significant improvements in network-wide travel time of up to 9% with real-time computational performance.

scholarly journals A Spatio-Temporal Visualization Approach of PM10 Concentration Data in Metropolitan Lima

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 609
Author(s):  
Alexandra Abigail Encalada-Malca ◽  
Javier David Cochachi-Bustamante ◽  
Paulo Canas Rodrigues ◽  
Rodrigo Salas ◽  
Javier Linkolk López-Gonzales
Keyword(s):  
Air Quality ◽  
Temporal Correlation ◽  
Quality Standard ◽  
Temporal Variations ◽  
Particulate Material ◽  
Pm10 Concentration ◽  
Spatial Behavior ◽  
Concentration Data ◽  
Visualization System ◽  
Spatio Temporal

Lima is considered one of the cities with the highest air pollution in Latin America. Institutions such as DIGESA, PROTRANSPORTE and SENAMHI are in charge of permanently monitoring air quality; therefore, the air quality visualization system must manage large amounts of data of different concentrations. In this study, a spatio-temporal visualization approach was developed for the exploration of data of the PM10 concentration in Metropolitan Lima, where the spatial behavior, at different time scales, of hourly concentrations of PM10 are analyzed using basic and specialized charts. The results show that the stations located to the east side of the metropolitan area had the highest concentrations, in contrast to the stations located in the center and north that reported better air quality. According to the temporal variation, the station with the highest average of biannual and annual PM10 was the HCH station. The highest PM10 concentrations were registered in 2018, during the summer, highlighting the month of March with daily averages that reached 435 μμg/m3. During the study period, the CRB was the station that recorded the lowest concentrations and the only one that met the Environmental Quality Standard for air quality. The proposed approach exposes a sequence of steps for the elaboration of charts with increasingly specific time periods according to their relevance, and a statistical analysis, such as the dynamic temporal correlation, that allows to obtain a detailed visualization of the spatio-temporal variations of PM10 concentrations. Furthermore, it was concluded that the meteorological variables do not indicate a causal relationship with respect to PM10 levels, but rather that the concentrations of particulate material are related to the urban characteristics of each district.

scholarly journals Developing a Modular Unmanned Aerial Vehicle (UAV) Platform for Air Pollution Profiling

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4363 ◽  
Author(s):  
Qijun Gu ◽  
Drew R. Michanowicz ◽  
Chunrong Jia
Keyword(s):  
Air Quality ◽  
Data Fusion ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
High Precision ◽  
Flight Control ◽  
Quality Data ◽  
Sample Collection ◽  
Ground Station ◽  
Aerial Vehicle

The unmanned aerial vehicle (UAV) offers great potential for collecting air quality data with high spatial and temporal resolutions. The objective of this study is to design and develop a modular UAV-based platform capable of real-time monitoring of multiple air pollutants. The system comprises five modules: the UAV, the ground station, the sensors, the data acquisition (DA) module, and the data fusion (DF) module. The hardware was constructed with off-the-shelf consumer parts and the open source software Ardupilot was used for flight control and data fusion. The prototype UAV system was tested in representative settings. Results show that this UAV platform can fly on pre-determined pathways with adequate flight time for various data collection missions. The system simultaneously collects air quality and high precision X-Y-Z data and integrates and visualizes them in a real-time manner. While the system can accommodate multiple gas sensors, UAV operations may electronically interfere with the performance of chemical-resistant sensors. Our prototype and experiments prove the feasibility of the system and show that it features a stable and high precision spatial-temporal platform for air sample collection. Future work should be focused on gas sensor development, plug-and-play interfaces, impacts of rotor wash, and all-weather designs.

Sign in / Sign up

Export Citation Format

Share Document