Assessment of the Contribution of Different Particulate Matter Sources on Pollution in Sofia City

Dimitrova, Reneta; Velizarova, Margret

doi:10.3390/atmos12040423

Cited by 16 publications

(10 citation statements)

References 24 publications

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“…A recent study for Sofia, based on the Positive Matrix Factorization (PMF) receptor model for source contribution of different groups of emission sources to PM 10 concentrations [10], indicated 23% for biomass burning and 9% for transport on an annual basis for 2019. Another recent study on the determination of the local pollution of PM and PAH in the Sofia municipality by the Gaussian dispersion-based ADMS-Urban model showed that domestic heating contributed 59% of the PM 2.5 concentrations [48].…”

Section: Pm25 and Bc Emissionsmentioning

confidence: 99%

Black Carbon in Bulgaria—Observed and Modelled Concentrations in Two Cities for Two Months

et al. 2022

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Black carbon (BC) is one of the particulate matter (PM) components that both affects human health and contributes to climate change. In this study, we present the preliminary results of the investigation of BC concentrations in PM2.5 for two Bulgarian cites—Sofia and Burgas. The parallel PM2.5 samplings were organized in October 2020 and January 2021. The Multi-Wavelength Absorption Black carbon Instrument (MABI) was used for the evaluation of light-absorbing carbon. In addition, we compared the observed BC and PM2.5 values to modelled ones and analyzed the spatial distribution over the country, using data from advanced operational chemical transport models (CTM)—the European (regional) air quality system established at the Copernicus Atmosphere Monitoring Service (CAMS). Generally, the observed BC and PM2.5 values were higher in January than in October for both cities. In October, the model underestimated the observed BC concentrations (Sofia—2.44 μg.m−3, Burgas—1.63 μg.m−3) by 17% and 51%. In January 2021, the observed monthly BC concentrations were higher (Sofia—3.62 μg.m−3, Burgas—1.75 μg.m−3), and the bias of the model was less than that in October, with an overestimation of 22% for Sofia. The relative bias for PM2.5 in October (17% for Sofia and −6% for Burgas) was less than the relative bias in January when the model underestimated PM2.5 monthly mean concentrations by 20% (Sofia) and 42% (Burgas). In addition, we also elaborate on two episodes with high observed BC concentrations in view of the meteorological conditions.

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Section: Pm25 and Bc Emissionsmentioning

confidence: 99%

Black Carbon in Bulgaria—Observed and Modelled Concentrations in Two Cities for Two Months

et al. 2022

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“…Large floods and drought periods have increasingly taken place in the Upper Iskar Basin in the region of Sofia (Daniell 2011). PM10 concentrations in Bulgaria were one of the highest in 2009 and it continues to be the aforesaid nowadays (Dimitrova & Velizarova 2021). PM is particularly harmful during the winter period in big cities, such as Sofia, being domestic heating and transport emissions the main sources.…”

Section: Sofia Case Studymentioning

confidence: 99%

Using open data to reveal factors of urban susceptibility to natural hazards and man-made hazards: case of Milan and Sofia

2022

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Multi-hazard mapping in urban areas is relevant for preventing and mitigating the impact of nature- and human-induced disasters while being a challenging task as different competencies have to be put together. Artificial intelligence models are being increasingly exploited for single-hazard susceptibility mapping, from which multi-hazard maps are ultimately derived. Despite the remarkable performance of these models, their application requires the identification of a list of conditioning factors as well as the collection of relevant data and historical inventories, which may be non-trivial tasks. The objective of this study is twofold. First, based on a review of recent publications, it identifies conditioning factors to be used as an input to machine and deep learning techniques for singlehazard susceptibility mapping. Second, it investigates open datasets describing those factors for two European cities, namely Milan (Italy) and Sofia (Bulgaria) by exploiting local authorities’ databases. Identification of the conditioning factors was carried out through the review of recent publications aiming at hazard mapping with artificial intelligence models. Two indicators were conceived to define the relevance of each factor. A first research result consists of a relevance-sorted list of conditioning factors per hazard as well as a set of open and free access data describing several factors for Milan and Sofia. Based on data availability, a feasibility analysis was carried out to investigate the possibility to model hazard susceptibility for the two case studies as well as for the limit case of a city with no local data available. Results show major differences between Milan and Sofia while pointing out Copernicus services’ datasets as a valuable resource for susceptibility mapping in case of limited local data availability. Achieved outcomes have to be intended as preliminary results, as further details shall be disclosed after the discussion with domain experts.

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“…The aim of our study investigating the relationship between daily air pollution levels and hospital admissions for ischaemic heart disease, cerebrovascular accidents, and diabetes mellitus in Sofia, Bulgaria, was to expand its scope by including 10 years worth of data on hospital admissions and air quality, using multivariate analysis techniques suitable for modelling autocorrelated count data. Sofia is the capital city of Bulgaria characterised by high population density, heavy traffic, and heavy use of fossil fuel for household heating, coupled with poor ventilation and temperature inversions due to its location in a mountain basin ( 17 , 22 ).…”

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confidence: 99%

Short-term effects of air pollution on hospital admissions for cardiovascular diseases and diabetes mellitus in Sofia, Bulgaria (2009–2018)

Dzhambov

Dikova

Georgieva

et al. 2023

Archives of Industrial Hygiene and Toxicology

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Bulgaria has a very high incidence of cardiometabolic diseases and air pollution-related mortality rate. This study investigated the relationship between daily air pollution levels and hospital admissions for ischaemic heart diseases (IHD), cerebral infarction (CI), and type 2 diabetes mellitus (T2DM) in Sofia, Bulgaria. We obtained daily data on hospitals admissions and daily average air pollution levels from 2009 to 2018. Pollutants of interest were particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2), ozone (O3), and carbon monoxide (CO). Negative binomial regressions were fitted to study the effects of air pollution on hospital admission over the course of seven days prior to that event, accounting for autocorrelations and time trend in the data, day of the week, temperature, and relative humidity. Our findings confirm that higher air pollution levels generally increase the risk of hospital admissions for IHD and CI. For T2DM the association is less clear. Admissions often lagged several days behind and were more common in specific demographic subgroups or when pollution crossed a particular threshold. However, we did not expect to find the risk of hospital admissions increased in warmer rather than colder months of the year. Our findings are to be taken with reservation but do provide an idea about how air pollution could trigger acute episodes of related cardiovascular diseases, and our model may serve to investigate similar associations across the country.

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Assessment of the Contribution of Different Particulate Matter Sources on Pollution in Sofia City

Cited by 16 publications

References 24 publications

Black Carbon in Bulgaria—Observed and Modelled Concentrations in Two Cities for Two Months

Black Carbon in Bulgaria—Observed and Modelled Concentrations in Two Cities for Two Months

Using open data to reveal factors of urban susceptibility to natural hazards and man-made hazards: case of Milan and Sofia

Short-term effects of air pollution on hospital admissions for cardiovascular diseases and diabetes mellitus in Sofia, Bulgaria (2009–2018)

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