scholarly journals Source Apportionment of PM2.5, PAH and Arsenic Air Pollution in Central Bohemia

Environments ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 107
Author(s):  
Radim Seibert ◽  
Irina Nikolova ◽  
Vladimíra Volná ◽  
Blanka Krejčí ◽  
Daniel Hladký
Keyword(s):  
Air Quality ◽  
Source Apportionment ◽  
Road Traffic ◽  
Region Of Interest ◽  
Ambient Air ◽  
Residential Heating ◽  
Aerosol Mass ◽  
Area Of Interest ◽  
Coal Fuel ◽  
Polycyclic Aromatic

The results of air quality monitoring show significantly increased concentrations of polycyclic aromatic hydrocarbons (PAH) and arsenic in the area located near the town of Kladno in Central Bohemia, Czech Republic. The region of interest is historically associated with coal mines and steelworks. Source apportionment using the method of Positive Matrix Factorization (PMF) at three sites has been used to try to explain the reasons of the increased PM2.5, benzo[a]pyrene, and arsenic concentrations in the ambient air. Based on the PMF analysis, nine factors explaining the atmospheric aerosol mass have been identified. The PMF results showed that most of the aerosol mass originated from residential heating (about one third of PM2.5), both primary particles and secondary organic aerosols induced by road traffic (up to approximately 25%), soil and other mineral dust (about 15%), secondary inorganic aerosol ammonium sulfate (up to 16%), ammonium nitrate (up to 14%) and other sulfates (up to 9%). The main source of arsenic and benzo[a]pyrene was residential heating, which accounted for two-thirds and 80% of their total mass, respectively. The results have pointed to the most important measures for effective air quality protection in the area of interest: replacing coal fuel and old boilers used for residential heating in order to reduce arsenic and PAH emissions and mitigate sources of secondary particles precursors to decrease PM concentrations.

scholarly journals Organic aerosol source apportionment by offline-AMS over a full year in Marseille

2017 ◽  
Author(s):  
Carlo Bozzetti ◽  
Imad El Haddad ◽  
Dalia Salameh ◽  
Kaspar Rudolf Daellenbach ◽  
Paola Fermo ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Mediterranean Sea ◽  
Source Apportionment ◽  
Organic Aerosol ◽  
Aerodynamic Diameter ◽  
Aerosol Mass Spectrometer ◽  
Aerosol Mass ◽  
Aerosol Source

Abstract. We investigated the seasonal trends of OA sources affecting the air quality of Marseille (France) which is the largest harbor of the Mediterranean Sea. This was achieved by measurements of nebulized filter extracts using an aerosol mass spectrometer (offline-AMS). PM2.5 (particulate matter with an aerodynamic diameter

Day-to-day variations in paddy-residue burning and residential heating emissions control aerosol pollution peaks in rural north-west India

2021 ◽  
Author(s):  
Harshita Pawar ◽  
Baerbel Sinha
Keyword(s):  
Air Quality ◽  
Large Scale ◽  
Learning Algorithm ◽  
Low Cost ◽  
Winter Season ◽  
Ambient Air ◽  
Rural Site ◽  
North West ◽  
Daily Average ◽  
Residential Heating

<p>November onwards, the poor air quality over north-west India is blamed on the large-scale paddy residue burning in Punjab and Haryana. However, the emission strength of this source remains poorly constrained due to the lack of ground-based measurements within the rural source regions. In this study, we report the particulate matter (PM) levels at Nadampur, a rural site in the Sangrur district of Punjab that witnesses rampant paddy residue burning, using the Airveda low-cost PM sensors from October to December 2019. The raw PM measurements from the sensor were corrected using the Random Forest machine learning algorithm. The daily average PM<sub>10</sub> and PM<sub>2.5</sub> mass concentration at Nadampur correlated well  (r > 0.7) with the daily sum of VIIRS fire counts. Agricultural activities, including paddy residue burning and harvesting operations, contributed less than 40% to the overall PM loading, even in the peak burning period at Nadampur. We show that the increased residential heating emissions in the winter season have a profound and currently neglected impact on ambient air quality. A dip in the daily average temperature by 1 ºC increased the daily emission of PM<sub>10</sub> by 6.3 tonnes and that of PM<sub>2.5</sub> by 5.8 tonnes. Overall, paddy harvest, local and regional paddy residue burning, residential heating emissions, ventilation, and wet scavenging could explain 79% of the variations in PM<sub>10</sub> and 85% of the variations in PM<sub>2.5</sub>. Day to day variations in PM emissions from residential heating in response to the ambient temperature must be incorporated into emission inventories and models for accurate air quality forecasts.</p>

scholarly journals LIFE MONZA: project description and actions’ updating

Noise Mapping ◽  
2018 ◽  
Vol 5 (1) ◽  
pp. 60-70 ◽  
Author(s):  
Chiara Bartalucci ◽  
Francesco Borchi ◽  
Monica Carfagni ◽  
Rocco Furferi ◽  
Lapo Governi ◽  
...  
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Air Pollutants ◽  
Road Traffic ◽  
Ambient Air ◽  
Limit Values ◽  
Low Emission ◽  
Project Description ◽  
Air Quality Improvement ◽  
The City

Abstract The introduction of Low Emission Zones, urban areas subject to road traffic restrictions in order to ensure compliance with the air pollutants limit values set by the European Directive on ambient air quality (2008/50/EC), is a common and well-established action in the administrative government of cities. The impacts on air quality improvement are widely analysed, whereas the effects and benefits concerning the noise have not been addressed in a comprehensive manner. As a consequence, the definition, the criteria for the analysis and the management methods of a Noise Low Emission Zone are not clearly expressed and shared yet. The LIFE MONZA project (Methodologies fOr Noise low emission Zones introduction And management - LIFE15 ENV/IT/000586) addresses these issues. The first objective of the project, co-funded by the European Commission, is to introduce an easy-replicable method for the identification and the management of the Noise Low Emission Zone, an urban area subject to traffic restrictions, whose impacts and benefits regarding noise issues will be analyzed and tested in the pilot area of the city of Monza, located in Northern Italy. Background conditions, structure, objectives of the project and actions’ progress will be discussed in this article.

scholarly journals PM2.5 Pollution in Xingtai, China: Chemical Characteristics, Source Apportionment, and Emission Control Measures

Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 121 ◽  
Author(s):  
Jun Hu ◽  
Han Wang ◽  
Jingqiao Zhang ◽  
Meng Zhang ◽  
Hefeng Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Source Apportionment ◽  
Urban Areas ◽  
Emission Control ◽  
Clean Energy ◽  
Ambient Air ◽  
Control Measures ◽  
Ambient Air Quality ◽  
Surrounding Areas ◽  
Emission Control Measures

Beijing-Tianjin-Hebei (BTH) and its surrounding areas are one of the most polluted regions in China. Xingtai, as a heavy industrial city of BTH and its surrounding areas, has been experiencing a severe PM2.5 pollution in recent years, characterized by extremely high concentrations of PM2.5. In 2014, PM2.5 mass concentrations monitored by online instruments in urban areas of Xingtai were 116, 77, 128, and 200 µg m−3 in spring, summer, autumn and winter, respectively, with annually average concentrations of 130 µg m−3 exhibiting 3.7 times higher than National Ambient Air Quality Standard (NAAQS) value for PM2.5 (35 µg m−3). To identify PM2.5 emission sources, ambient PM2.5 samples were collected during both cold and warm periods in 2014 in urban areas of Xingtai. Organic carbon (OC), sulfate, nitrate, ammonium and elemental carbon (EC) were the dominant components of PM2.5, accounting for 13%, 11%, 12%, 11% and 8% in the cold period, respectively, and 11%, 12%, 9%, 6%, and 5% in the warm period, respectively. Source apportionment results indicated that coal combustion (24.4%) was the largest PM2.5 emission source, followed by secondary sulfate (22.2%), secondary nitrate (18.4%), vehicle exhaust dust (12.4%), fugitive dust (9.7%), construction dust (5.5%), soil dust (3.4%) and metallurgy dust (1.6%). Based on PM2.5 source apportionment results, some emission control measures, such as replacing bulk coal with clean energy sources, controlling coal consumption by coal-fired boiler upgrades, halting operations of unlicensed small polluters, and controlling fugitive and VOCs emission, were proposed to be implemented in order to improve Xingtai’s ambient air quality.

scholarly journals Introduction and Application of the PMF Model to Estimate the Source Apportionment of PM2.5 at Various Sites

1970 ◽  
Vol 8 (3) ◽  
pp. 25-31 ◽  
Author(s):  
Injo Hwang
Keyword(s):  
Air Quality ◽  
Source Apportionment ◽  
Receptor Site ◽  
Ambient Air ◽  
Receptor Modeling ◽  
Ambient Air Quality ◽  
Receptor Models ◽  
Pmf Model ◽  
Source Contributions ◽  
Reduction Strategies

To manage ambient air quality and establish effective emissions reduction strategies, it is necessary to identify sources and to apportion the ambient PM mass. To do so, receptor models have been developed that analyze various measured properties of the pollutants at the receptor site, identify the sources, and estimate their contributions. Receptor modeling is based on a mathematical model that analyzes the physicochemical properties of gaseous and/or particulate pollutants at various atmospheric receptors. Among the multivariate receptor models used for PM source identification and apportionment, positive matrix factorization (PMF) has been developed by Paatero in 1997. PMF have been developed for providing a new approach to multivariate receptor modeling based on explicit least-squares technique. Also, PMF shown to be a powerful technique relative to traditional multivariate receptor models. PMF has been implemented in two different algorithms: PMF2 (or PMF3) and the multilinear engine (ME). Since the release of PMF2 and ME, these programs have been successfully applied to assess ambient PM source contributions at many locations in the world. In this study, I would like to introduce about outline of the PMF model and application of the PMF model to estimate the source apportionment of ambient PM2.5 at various sampling sites in USA and Korea. This study suggests the possible role for maintain and manage ambient air quality and achieve reasonable air pollution strategies. DOI: http://dx.doi.org/10.3126/jie.v8i3.5928 JIE 2011; 8(3): 25-31

Open air mineral treatment operations and ambient air quality: assessment and source apportionment

2012 ◽  
Vol 14 (11) ◽  
pp. 2939 ◽  
Author(s):  
M. Escudero ◽  
A. Alastuey ◽  
T. Moreno ◽  
X. Querol ◽  
P. Pérez
Keyword(s):  
Air Quality ◽  
Quality Assessment ◽  
Source Apportionment ◽  
Ambient Air ◽  
Ambient Air Quality ◽  
Air Quality Assessment

scholarly journals Source Apportionment and Integrated Assessment Modelling for Air Quality Planning

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1098
Author(s):  
Elena De Angelis ◽  
Claudio Carnevale ◽  
Enrico Turrini ◽  
Marialuisa Volta
Keyword(s):  
Air Quality ◽  
Source Apportionment ◽  
Integrated Assessment ◽  
Road Transport ◽  
Northern Italy ◽  
Optimization Approach ◽  
Quality Planning ◽  
Residential Heating ◽  
Integrated Assessment Modelling ◽  
Air Quality Planning

In Northern Italy a large fraction of the population is exposed to PM10 and PM2.5 concentrations that exceed the European limit values and the stricter WHO air quality guidelines. For this reason, in 2017 four Regions (Piemonte, Lombardia, Veneto, and Emilia Romagna) and the national Ministry of the Environment adopted a set of joint measures, namely the “Po Basin air quality plan”. The plan mainly tackles emission from road transport, residential heating, and agriculture. Air quality plans at regional and local scale are usually implemented defining a set of emission abatement measures, starting from experts’ knowledge. The aim of this work is to define a methodology that helps decision makers in air quality planning, combining two different approaches: Source-Apportionment techniques (SA) and Integrated Assessment Modelling (IAM). These techniques have been applied over a domain in Northern Italy to analyze the contribution of emission sources on PM10 concentration and to compute an optimal policy, obtained through a multi-objective optimization approach that minimizes both the PM10 yearly average concentration and the policy implementation costs. The results are compared to the Po Basin air quality plan impacts. The source-apportionment technique and the IAM optimization approach show intervention priorities in three main sectors: residential heating, agriculture, and road transport. The Po Basin air quality plan is effective in reducing PM10 concentrations, but not efficient, as a matter of fact the cost-effective policy at the same cost has a higher impact on air quality and on greenhouse gases emissions reduction.

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