scholarly journals Sources of Selected Chemical Components in Atmospheric Components in Finland

2021 ◽  
Author(s):  
◽  
Mika Vestenius
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Boreal Forest ◽  
Organic Compounds ◽  
Matrix Factorization ◽  
The Arctic ◽  
Positive Matrix ◽  
Natural Sources ◽  
Helsinki Metropolitan Area

Air pollution is an important environmental risk to human health and ecosystems around the world. Particulate matter (PM), especially fine particulate matter, is an important part of this air pollution problem. Particle composition varies greatly and depends on the emission source. In addition to inorganic components, organic particulate fraction can contain several hundred organic compounds from anthropogenic and natural sources. The health risk of particulate is related to the particle size and the compounds inside or on the surface of the aerosol particles. The overall aim of this thesis was to study the selected chemical substances of atmospheric aerosol from both anthropogenic and natural sources. Concentrations of polycyclic aromatic hydrocarbons (PAH) and biogenic organic acids in aerosol were measured, and their effect on the local air quality was estimated. The sources of PAHs, trace elements, biogenic volatile organic compounds (BVOCs), and persistent organic compounds (POPs) in air were studied using positive matrix factorization (PMF), which was used as the main source apportionment tool in three of five papers and for the unpublished data in this thesis. Particles from burning emissions, e.g., diesel particles and particles from biomass burning, are the most toxic in our daily environment. Because of intensive wood use for heating and in sauna stoves, residential biomass burning is the major PAH air pollution source in Finland. Sources of atmospheric PAH pollution and its influence on local air quality were estimated at Virolahti background air quality station and in the Helsinki Metropolitan Area (HMA). The main source of PAHs at Virolahti were found to be combustion- and traffic-related source from the direction of St. Petersburg. Instead, local traffic appeared to have a very small influence on PAH levels in HMA, as local residential wood burning was found to be the main b(a)p source in Helsinki Metropolitan Area. Biogenic VOCs like monoterpenes and sesquiterpenes are highly reactive and oxidize rapidly in the atmosphere, producing secondary organic aerosol (SOA). We showed that positive matrix factorization (PMF) is a useful tool in estimating separate sources in a quasistationary dynamic system like ambient VOC concentrations in the boreal forest. Selected biogenic organic acids were measured from fine particles in the boreal forest in order to estimate their influence on aerosol production. Results indicated that sesquiterpene emissions from boreal forest are probably underestimated and their oxidation products probably have more important role in the SOA production that previously estimated. The Kola Peninsula area was found to be the major source of heavy metal pollution at Pallas. However, as Norilsk Nickel has now partly shut down its metallurgical operations, the trace element and SO2 emissions from the Kola Peninsula should be declining in the future. The ambient concentrations of POP compounds are globally declining but, in the Arctic, for some compounds this is not the case. In the source apportionment study for Pallas 1996–2018 POPs data, relatively big portion of measured POPs at Pallas came within the marine source from clean areas from the north. These long-lived compounds, which have migrated into the Arctic from the southern areas along the air and sea currents for many decades, are now released back into the atmosphere from the melting Arctic ice cover due to global warming. For these compounds, the Arctic has turned from the sink to the source.

scholarly journals Analisis Karaterisasi Konsentrasi dan Komposisi Partikulat Udara (Studi Case : Surabaya)

2016 ◽  
Vol 2 (2) ◽  
pp. 97-103 ◽  
Author(s):  
Eka Fithriani Ahmad ◽  
Muhayatun Santoso
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Black Carbon ◽  
Matrix Factorization ◽  
Positive Matrix Factorization ◽  
Quality Standard ◽  
Receptor Modeling ◽  
Positive Matrix ◽  
Pm 2.5 ◽  
The Impact

Abstrak Pencemaran udara merupakan dampak yang sangat merugikan, tidak hanya bagi manusia tetapi juga akan berdampak buruk bagi ekosistem hewan dan tumbuhan. Pada penelitian ini akan mengkaji pencemaran udara dari Oktober 2012 hingga Februari 2014 melalui penelitian konsentrasi dan komposisi dari partikulat udara dengan ukuran PM 2.5. Penelitian ini bertujuan untuk menentuan sumber asal pencemaran di Surabaya sehingga dapat dijadikan referensi berbasis ilmiah sebagai langkah untuk membuat keputusan dan kebijakan yang tepat dalam menanggulangi dampak pencemaran. Metode pengolahan data dalam penelitian ini adalah dengan menggunakan analisis reseptor modeling yaitu Positif Matrix Factorization (PMF) untuk mengetahui sumber asal pencemaran. Hasil pengukuran yang diperoleh pada konsentrasi PM 2,5 adalah 15.05 μg/m3 sehingga telah melebihi baku mutu tahunan yang telah ditetapkan PP 41 tahun 1999, USEPA, maupun WHO. Dalam partikulat terdapat konsentrasi black carbon (BC) sebesar 3.20 μg/m3 dan unsur Pb dengan konsentrasi 0.28 μg/m3 yang telah melebihi nilai baku mutu USEPA. Sedangkan hasil analisis reseptor modeling di dapatkan sumber asal polutan berasal dari biomass, vehicle, soil, industri Pb, industri Zn dan indutri Fe. Kata kunci: Partikulat mater 2.5, black carbon, Pb, positive matrix factorization, Surabaya   Abstract Air pollution is a very adverse impact, not only for humans but also the ecosystem of plants and animals. This research examine air pollution from October 2012 until February 2014 through the research of concentration and composition of airborne particulates with a size of PM 2.5 μm. This study aims to determine the origin and location of pollution sources in Surabaya so that it can be used as scientific reference as a step to make the right decisions and policies in tackling the impact of pollution. Data processing method in this research used analysis of receptor modeling that is Positive Matrix Factorization (PMF) to determine the source of the pollution. Results obtained at a concentration of PM 2.5 was 15.05 μg/m3 so PM 2.5 has exceeded the quality standard yearly, based on PP 41 1999, USEPA and WHO. There are 3.20 μg/m3 concentration of black carbon (BC), element Pb in particulate matter with a concentration of 0.28 μg/m3 which has exceeded the value of the quality standard USEPA. The source of the pollutants come from biomass, vehicle, soil, industrial Pb, Zn and industries Fe industry.   Keywords: Particulate matter 2.5, black carbon, Pb, positive matrix factorization, Surabaya DOI: http://dx.doi.org/10.15408/jkv.v0i0.3602

scholarly journals Measurement report: Ambient volatile organic compounds (VOCs) pollution at urban Beijing: characteristics, sources, and implications for pollution control

2021 ◽  
Author(s):  
Lulu Cui ◽  
Di Wu ◽  
Shuxiao Wang ◽  
Qingcheng Xu ◽  
Ruolan Hu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Organic Compounds ◽  
Pollution Control ◽  
Matrix Factorization ◽  
Diesel Exhaust ◽  
Sampling Period ◽  
Control Measures ◽  
Biomass Combustion ◽  
Positive Matrix ◽  
Volatile Organic

Abstract. The increasing ozone (O3) pollution and high fraction of secondary organic aerosols (SOA) in fine particle mass highlighted the importance of volatile organic compounds (VOCs) in air pollution control. In this work, a campaign of comprehensive field observations was conducted at an urban site in Beijing, from December 2018 to November 2019, to identify the composition, sources, and secondary transformation potential of VOCs. The total mixing ratio of the 95 quantified VOCs (TVOC) observed in this study ranged from 5.5–118.7 ppbv with the mean value of 34.9 ppbv, and the contemporaneous mixing ratios of TVOC was significantly lower than those observed in 2014 and 2016, confirming the effectiveness of VOCs emission control measures in Beijing in recent years. Alkanes, OVOCs and halocarbons were the dominant chemical groups, accounting for 75–81 % of the TVOCs across the sampling months. High and low-O3/PM2.5 months as well as several O3/PM2.5 polluted days were identified during the sampling period. By deweathered calculation, we found that high O3/PM2.5 levels were due to both enhanced precursor emission levels and meteorological conditions favorable to O3 and PM2.5 production. The molar ratios of VOCs to NOX indicated that O3 formation was limited by VOCs during the whole sampling period. Diesel exhaust and industrial emission were identified as the major VOCs sources on both O3-polluted and PM2.5-polluted days based on positive matrix factorization (PMF) analysis, accounting for 46 % and 53 %, respectively. Moreover, higher proportion of oil/gas evaporation was observed on O3-polluted days (18 %) than that on O3-clean days (13 %), and higher proportion of coal/biomass combustion was observed on PM2.5-polluted days (18 %) than that on PM2.5-clean days (13 %). On the base of O3 formation impact, VOCs from fuel evaporation and diesel exhaust particularly toluene, xylenes, trans-2-butene, acrolein, methyl methacrylate, vinyl acetate, 1-butene and 1-hexene were the main contributors, illustrating the necessity of conducting emission controls on these pollution sources and species for alleviating O3 pollution. Instead, VOCs from diesel exhaust and coal/biomass combustion were found to be the dominant contributors for secondary organic aerosol formation potential (SOAFP), particularly the VOC species of toluene, 1-hexene, xylenes, ethylbenzene and styrene, and top priority should be given to these for the alleviation of haze pollution. The positive matrix factorization (PSCF) analysis showed that O3 and PM2.5 pollution was mainly affected by local emissions. This study provides insights for government to formulate effective VOCs control measures for air pollution in Beijing.

scholarly journals Particulate matter air pollution offsets ozone damage to global crop production

2017 ◽  
Author(s):  
Luke D. Schiferl ◽  
Colette L. Heald
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Crop Production ◽  
Surface Ozone ◽  
Diffuse Radiation ◽  
Environmental Pressures ◽  
Global Food Security ◽  
Negative Effect ◽  
Pollution Exposure

Abstract. Ensuring global food security requires a comprehensive understanding of environmental pressures on food production, including the impacts of air quality. Surface ozone damages plants and decreases crop production; this effect has been extensively studied. In contrast, the presence of particulate matter (PM) in the atmosphere can be beneficial to crops given that enhanced light scattering leads to a more even and efficient distribution of photons which can outweigh total incoming radiation loss. This study quantifies the impacts of ozone and PM on the global production of maize, rice, and wheat in 2010 and 2050. We show that accounting for the growing season of these crops is an important factor in determining their air pollution exposure. We find that the effect of PM can offset much, if not all, of the reduction in yield associated with ozone damage. Assuming maximum sensitivity to PM, the current (2010) global net impact of air quality on crop production is positive (+6.0 %, +0.5 %, and +4.9 % for maize, wheat, and rice, respectively). Future emissions scenarios indicate that attempts to improve air quality can result in a net negative effect on crop production in areas dominated by the PM effect. However, we caution that the uncertainty in this assessment is large due to the uncertainty associated with crop response to changes in diffuse radiation; this highlights that more detailed physiological study of this response for common cultivars is crucial.

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 Effect of the uncertainty in meteorology on air quality model predictions

Időjárás ◽  
2021 ◽  
Vol 125 (4) ◽  
pp. 625-646
Author(s):  
Zita Ferenczi ◽  
Emese Homolya ◽  
Krisztina Lázár ◽  
Anita Tóth
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Model System ◽  
Air Quality Model ◽  
Quality Model ◽  
Near Surface ◽  
The Impact

An operational air quality forecasting model system has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the area of Hungary and three big cites of the country (Budapest, Miskolc, and Pécs). The core of the model system is the CHIMERE off-line chemical transport model. The AROME numerical weather prediction model provides the gridded meteorological inputs for the chemical model calculations. The horizontal resolution of the AROME meteorological fields is consistent with the CHIMERE horizontal resolution. The individual forecasted concentrations for the following 2 days are displayed on a public website of the Hungarian Meteorological Service. It is essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input meteorological fields. The main aim of this research is to probe the response of an air quality model to its uncertain meteorological inputs. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. During the past decades, meteorological ensemble modeling has received extensive research and operational interest because of its ability to better characterize forecast uncertainty. One such ensemble forecast system is the one of the AROME model, which has an 11-member ensemble where each member is perturbed by initial and lateral boundary conditions. In this work we focus on wintertime particulate matter concentrations, since this pollutant is extremely sensitive to near-surface mixing processes. Selecting a number of extreme air pollution situations we will show what the impact of the meteorological uncertainty is on the simulated concentration fields using AROME ensemble members.

Research Insights: What will People Pay for SMS Air Quality Alerts and Will They Avoid Air Pollution in Response?

2021 ◽  
Author(s):  
Rema Hanna ◽  
Bridget Hoffmann ◽  
Paulina Oliva ◽  
Jake Schneider
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Willingness To Pay ◽  
The Past ◽  
High Pollution

Male, younger, and higher-income respondents as well as those who perceived high pollution in recent days showed greater willingness to pay for SMS air quality alerts. Willingness to pay was uncorrelated with actual recent high pollution. Recipients of SMS alerts indicated having received air pollution information via SMS, along with reporting a high-pollution day in the past week and having stayed indoors on the most recent day they perceived pollution to be high. However, alert recipients were not more accurate in identifying which specific days had high pollution than other respondents. Households that received a free N95 mask were more likely to report utilizing a mask with a filter during the past two weeks but not more likely to report using a mask with a filter on the specific days with high particulate matter.

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