Evolution of air pollution source contributions over one decade, derived by PM10 and PM2.5 source apportionment in two metropolitan urban areas in Greece

2017 ◽  
Vol 164 ◽  
pp. 416-430 ◽  
Author(s):  
E. Diapouli ◽  
M. Manousakas ◽  
S. Vratolis ◽  
V. Vasilatou ◽  
Th Maggos ◽  
...  
Keyword(s):  
Air Pollution ◽  
Source Apportionment ◽  
Urban Areas ◽  
Pollution Source ◽  
Pm10 And Pm2.5 ◽  
Source Contributions

scholarly journals AIRUSE-LIFE+: a harmonized PM speciation and source apportionment in five southern European cities

2016 ◽  
Vol 16 (5) ◽  
pp. 3289-3309 ◽  
Author(s):  
Fulvio Amato ◽  
Andrés Alastuey ◽  
Angeliki Karanasiou ◽  
Franco Lucarelli ◽  
Silvia Nava ◽  
...  
Keyword(s):  
Air Quality ◽  
Source Apportionment ◽  
Biomass Burning ◽  
Urban Areas ◽  
The Other ◽  
Saharan Dust ◽  
Industrial Emissions ◽  
Large Variability ◽  
Pm10 And Pm2.5 ◽  
Local Dust

Abstract. The AIRUSE-LIFE+ project aims at characterizing similarities and heterogeneities in particulate matter (PM) sources and contributions in urban areas from southern Europe. Once the main PMx sources are identified, AIRUSE aims at developing and testing the efficiency of specific and non-specific measures to improve urban air quality. This article reports the results of the source apportionment of PM10 and PM2.5 conducted at three urban background sites (Barcelona, Florence and Milan, BCN-UB, FI-UB and MLN-UB), one suburban background site (Athens, ATH-SUB) and one traffic site (Porto, POR-TR). After collecting 1047 PM10 and 1116 PM2.5 24 h samples during 12 months (from January 2013 on) simultaneously at the five cities, these were analysed for the contents of OC, EC, anions, cations, major and trace elements and levoglucosan. The USEPA PMF5 receptor model was applied to these data sets in a harmonized way for each city. The sum of vehicle exhaust (VEX) and non-exhaust (NEX) contributes between 3.9 and 10.8 µg m−3 (16–32 %) to PM10 and 2.3 and 9.4 µg m−3 (15–36 %) to PM2.5, although a fraction of secondary nitrate is also traffic-related but could not be estimated. Important contributions arise from secondary particles (nitrate, sulfate and organics) in PM2.5 (37–82 %) but also in PM10 (40–71 %), mostly at background sites, revealing the importance of abating gaseous precursors in designing air quality plans. Biomass burning (BB) contributions vary widely, from 14–24 % of PM10 in POR-TR, MLN-UB and FI-UB, 7 % in ATH-SUB, to  <  2 % in BCN-UB. In PM2.5, BB is the second most important source in MLN-UB (21 %) and in POR-TR (18 %), the third one in FI-UB (21 %) and ATH-SUB (11 %), but is again negligible (< 2 %) in BCN-UB. This large variability among cities is mostly due to the degree of penetration of biomass for residential heating. In Barcelona natural gas is very well supplied across the city and is used as fuel in 96 % of homes, while in other cities, PM levels increase on an annual basis by 1–9 µg m−3 due to biomass burning influence. Other significant sources are the following. – Local dust, 7–12 % of PM10 at SUB and UB sites and 19 % at the TR site, revealing a contribution from road dust resuspension. In PM2.5 percentages decrease to 2–7 % at SUB-UB sites and 15 % at the TR site. – Industry, mainly metallurgy, contributing 4–11 % of PM10 (5–12 % in PM2.5), but only at BCN-UB, POR-TR and MLN-UB. No clear impact of industrial emissions was found in FI-UB and ATH-SUB. – Natural contributions from sea salt (13 % of PM10 in POR-TR, but only 2–7 % in the other cities) and Saharan dust (14 % in ATH-SUB, but less than 4 % in the other cities). During high pollution days, the largest sources (i.e. excluding secondary aerosol factors) of PM10 and PM2.5 are VEX + NEX in BCN-UB (27–22 %) and POR-TR (31–33 %), BB in FI-UB (30–33 %) and MLN-UB (35–26 %) and Saharan dust in ATH-SUB (52–45 %). During those days, there are also quite important industrial contributions in BCN-UB (17–18 %) and local dust in POR-TR (28–20 %).

scholarly journals Are cities responsible for their air pollution?

2021 ◽  
Author(s):  
Philippe Thunis ◽  
Alain Clappier ◽  
Alexander de Meij ◽  
Enrico Pisoni ◽  
Bertrand Bessagnet ◽  
...  
Keyword(s):  
Air Pollution ◽  
Source Apportionment ◽  
Urban Areas ◽  
Large Cities ◽  
Scientific Discussion ◽  
Methodological Choices ◽  
Average Factor ◽  
Single Number ◽  
A Minor ◽  
The City

Abstract. While the burden caused by air pollution in urban areas is well documented, the origin of this pollution and therefore the responsibility of the urban areas in generating this pollution is still a subject of scientific discussion. Source Apportionment represents a useful technique to quantify the city responsibility but the approaches and applications are not harmonized, therefore not comparable, resulting in confusing and sometimes contradicting interpretations. In this work, we analyze how different source apportionment approaches apply to the urban scale and how their building elements and parameters are defined and set. We discuss in particular the options available in terms of indicator, receptor, source and methodology. We show that different choices for these options lead to very large differences in terms of outcome. In average over the 150 EU large cities selected in our study, the choices made for the indicator, the receptor and the source each lead to an average factor 2 difference. We also show that temporal and spatial averaging processes applied to the air quality indicator, especially when diverging source apportionments are aggregated into a single number lead to favor strategies that target background sources while occulting actions that would be efficient at the city center. We stress that methodological choices and assumptions most often lead to a systematic and important underestimation of the city responsibility, with important implications. Indeed, if cities are seen as a minor actor, plans will target in priority the background at the expense of potentially effective local actions.

scholarly journals Spatiotemporal evolution analysis of NO2 column density before and after COVID-19 pandemic in Henan province based on SI-APSTE model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yang Liu ◽  
Jinhuan Zhao ◽  
Kunlin Song ◽  
Cheng Cheng ◽  
Shenshen Li ◽  
...  
Keyword(s):  
Air Pollution ◽  
Swarm Intelligence ◽  
Urban Areas ◽  
Pollution Source ◽  
Henan Province ◽  
Spatiotemporal Evolution ◽  
Trend Prediction ◽  
Vertical Column ◽  
Evolution Analysis ◽  
Before And After

AbstractAir pollution is the result of comprehensive evolution of a dynamic and complex system composed of emission sources, topography, meteorology and other environmental factors. The establishment of spatiotemporal evolution model is of great significance for the study of air pollution mechanism, trend prediction, identification of pollution sources and pollution control. In this paper, the air pollution system is described based on cellular automata and restricted agents, and a Swarm Intelligence based Air Pollution SpatioTemporal Evolution (SI-APSTE) model is constructed. Then the spatiotemporal evolution analysis method of air pollution is studied. Taking Henan Province before and after COVID-19 pandemic as an example, the NO2 products of TROPOMI and OMI were analysed based on SI-APSTE model. The tropospheric NO2 Vertical Column Densities (VCDs) distribution characteristics of spatiotemporal variation of Henan province before COVID-19 pandemic were studied. Then the tropospheric NO2 VCDs of TROPOMI was used to study the pandemic period, month-on-month and year-on-year in 18 urban areas of Henan Province. The results show that SI-APSTE model can effectively analyse the spatiotemporal evolution of air pollution by using environmental big data and swarm intelligence, and also can establish a theoretical basis for pollution source identification and trend prediction.

scholarly journals AIRUSE-LIFE+: a harmonized PM speciation and source apportionment in 5 Southern European cities

2015 ◽  
Vol 15 (17) ◽  
pp. 23989-24039 ◽  
Author(s):  
F. Amato ◽  
A. Alastuey ◽  
A. Karanasiou ◽  
F. Lucarelli ◽  
S. Nava ◽  
...  
Keyword(s):  
Air Quality ◽  
Source Apportionment ◽  
Urban Areas ◽  
The Other ◽  
Saharan Dust ◽  
Industrial Emissions ◽  
Urban Background ◽  
Large Variability ◽  
Pm10 And Pm2.5 ◽  
Local Dust

Abstract. The AIRUSE-LIFE+ project aims at characterising similarities and heterogeneities in PM sources and contributions in urban areas from the Southern Europe. Once the main PMx sources are identified, AIRUSE aims at developing and testing the efficiency of specific and non-specific measures to improve urban air quality. This article reports the results of the source apportionment of PM10 and PM2.5 conducted at three urban background sites (Barcelona, Florence and Milan, BCN-UB, FI-UB, MLN-UB) one sub-urban background site (Athens, ATH-SUB) and one traffic site (Porto, POR-TR). After collecting 1047 PM10 and 1116 PM2.5 24 h samples from January 2013 to February 2014 simultaneously at the 5 cities, these were analysed for the contents of OC, EC, anions, cations, major and trace elements and levoglucosan. The USEPA PMF5 receptor model was applied to these datasets in a harmonised way for each city. The sum of vehicle exhaust and non-exhaust contributes within 3.9–10.8 μg m−3 (16–32 %) to PM10 and 2.3–9.4 μg m−3 (15–36 %) to PM2.5, although a fraction of secondary nitrate is also traffic-related but could not be estimated. Important contributions arise from secondary particles (nitrate, sulphate and organics) in PM2.5 (37–82 %) but also in PM10 (40–71 %) mostly at background sites, revealing the importance of abating gaseous precursors in designing air quality plans. Biomass burning (BB) contributions vary widely, from 14–24 % of PM10 in POR-TR, MLN-UB and FI-UB, 7 % in ATH-SUB to < 2 % in BCN-UB. In PM2.5, BB is the second most important source in MLN-UB (21 %) and in POR-TR (18 %), the third one in FI-UB (21 %) and ATH-SUB (11 %), but again negligible (< 2 %) in BCN-UB. This large variability among cities is mostly due to the degree of penetration of biomass for residential heating. In Barcelona natural gas is very well supplied across the city and used as fuel in 96 % of homes, while, in other cities, PM levels increase on an annual basis by 1–9 μg m−3 due to this source. Other significant sources are: - Local dust, 7–12 % of PM10 at SUB and UB sites and 19 % at the TR site, revealing a contribution from road dust resuspension. In PM2.5 percentages decrease to 2–7 % at SUB-UB sites and 15 % at the TR site. - Industries, mainly metallurgy, contributing 4–11 % of PM10 (5–12 % in PM2.5), but only at BCN-UB, POR-TR and MLN-UB. No clear impact of industrial emissions was found in FI-UB and ATH-SUB. - Natural contributions from sea salt (13 % of PM10 in POR-TR but only 2–7 % in the other cities) and Saharan dust (14 % in ATH-SUB), but less than 4 % in the other cities. During high pollution days, the largest specific source (i.e. excluding SSO and SNI) of PM10 and PM2.5 are: VEX+NEX in BCN-UB (27–22 %) and POR-TR (31–33 %), BB in FI-UB (30–33 %) and MLN-UB (35–26 %) and Saharan dust in ATH-SUB (52–45 %) During those days, there are also quite important Industrial contributions in BCN-UB (17–18 %) and Local dust in POR-TR (28–20 %).

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