Air Quality Monitoring in Jiu Valley

Mining Revue ◽  
2021 ◽  
Vol 27 (3) ◽  
pp. 64-79
Author(s):  
Liliana Roman
Keyword(s):  
Air Quality ◽  
Quality Indicators ◽  
Rapid Identification ◽  
Pollution Sources ◽  
Quality Monitoring ◽  
Monitoring Station ◽  
Air Quality Monitoring ◽  
Quality Indices ◽  
Strategic Decisions ◽  
Low Level

Abstract The paper presents air quality monitoring in Jiu Valley, which is carried out at HD-5 Vulcan monitoring station, starting with March 2010, allowing the obtaining of useful data for the rapid identification of polluted areas and for taking strategic decisions by competent factors and tactics to combat and prevent pollution. After highlighting the pollution sources in Jiu Valley, we present both the evolution of hourly and / or daily values of pollutants recorded at the automatic HD-Vulcan air quality monitoring station during 2020: SO2, NO2, CO, gravimetric PM10, Pb, Cd and Ni, but also the evolution of air quality for quality indicators, monitored in Hunedoara County (including Jiu Valley, at HD-5 station), during 2010-2020. Taking into account the average annual values of pollutants recorded in 2020 at HD-5 Vulcan station, the paper calculates air quality indices for each pollutant and then indicates air quality throughout Jiu Valley in 2020, establishing that air quality is good in this area, with a very low level of pollution and no effect on humans, ecosystems and materials.

The Brera Tower air quality monitoring station

1988 ◽  
Vol 11 (5-6) ◽  
pp. 629-642 ◽  
Author(s):  
G. M. Marcazzan ◽  
G. Ravasini ◽  
A. Ventura ◽  
P. Bacci
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Monitoring Station ◽  
Air Quality Monitoring

scholarly journals Air Quality Measurements in Kitchener, Ontario, Canada Using Multisensor Mini Monitoring Stations

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 83
Author(s):  
Wisam Mohammed ◽  
Nicole Shantz ◽  
Lucas Neil ◽  
Tom Townend ◽  
Adrian Adamescu ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Monitoring ◽  
Monitoring Station ◽  
Air Quality Monitoring ◽  
Long Distance ◽  
Air Quality Measurements ◽  
Custom Made ◽  
The Impact ◽  
Quality Measurements ◽  
Monitoring Stations

The Region of Waterloo is the third fastest growing region in Southern Ontario in Canada with a population of 619,000 as of 2019. However, only one air quality monitoring station, located in a city park in Kitchener, Ontario, is currently being used to assess the air quality of the region. In September 2020, a network of AQMesh Multisensor Mini Monitoring Stations (pods) were installed near elementary schools in Kitchener located near different types of emission source. Data analysis using a custom-made long-distance scaling software showed that the levels of nitrogen oxides (NO and NO2), ground level ozone (O3), and fine particulate matter (PM2.5) were traffic related. These pollutants were used to calculate the Air Quality Health Index-Plus (AQHI+) at each location, highlighting the inability of the provincial air quality monitoring station to detect hotspot areas in the city. The case study presented here quantified the impact of the 2021 summer wildfires on the local air quality at a high time resolution (15-min). The findings in this article show that these multisensor pods are a viable alternative to expensive research-grade equipment. The results highlight the need for networks of local scale air quality measurements, particularly in fast-growing cities in Canada.

scholarly journals Representativeness of an air quality monitoring station for PM2.5 and source apportionment over a small urban domain

2020 ◽  
Vol 11 (2) ◽  
pp. 225-233 ◽  
Author(s):  
S. Yatkin ◽  
M. Gerboles ◽  
C.A. Belis ◽  
F. Karagulian ◽  
F. Lagler ◽  
...  
Keyword(s):  
Air Quality ◽  
Source Apportionment ◽  
Quality Monitoring ◽  
Monitoring Station ◽  
Air Quality Monitoring

scholarly journals A Higher-Order Graph Convolutional Network for Location Recommendation of an Air-Quality-Monitoring Station

2021 ◽  
Vol 13 (8) ◽  
pp. 1600
Author(s):  
Yu Kang ◽  
Jie Chen ◽  
Yang Cao ◽  
Zhenyi Xu
Keyword(s):  
Air Quality ◽  
Influential Factor ◽  
Quality Monitoring ◽  
Monitoring Station ◽  
Air Quality Monitoring ◽  
Inference Model ◽  
Monitoring Equipment ◽  
Location Recommendation ◽  
Correlation Degree ◽  
Station Location

The location recommendation of an air-quality-monitoring station is a prerequisite for inferring the air-quality distribution in urban areas. How to use a limited number of monitoring equipment to accurately infer air quality depends on the location of the monitoring equipment. In this paper, our main objective was how to recommend optimal monitoring-station locations based on existing ones to maximize the accuracy of a air-quality inference model for inferring the air-quality distribution of an entire urban area. This task is challenging for the following main reasons: (1) air-quality distribution has spatiotemporal interactions and is affected by many complex external influential factors, such as weather and points of interest (POIs), and (2) how to effectively correlate the air-quality inference model with the monitoring station location recommendation model so that the recommended station can maximize the accuracy of the air-quality inference model. To solve the aforementioned challenges, we formulate the monitoring station location as an urban spatiotemporal graph (USTG) node recommendation problem in which each node represents a region with time-varying air-quality values. We design an effective air-quality inference model-based proposed high-order graph convolution (HGCNInf) that could capture the spatiotemporal interaction of air-quality distribution and could extract external influential factor features. Furthermore, HGCNInf can learn the correlation degree between the nodes in USTG that reflects the spatiotemporal changes in air quality. Based on the correlation degree, we design a greedy algorithm for minimizing information entropy (GMIE) that aims to mark the recommendation priority of unlabeled nodes according to the ability to improve the inference accuracy of HGCNInf through the node incremental learning method. Finally, we recommend the node with the highest priority as the new monitoring station location, which could bring about the greatest accuracy improvement to HGCNInf.

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