scholarly journals Source Apportionment and Assessment of Air Quality Index of PM2.5–10 and PM2.5 in at Two Different Sites in Urban Background Area in Senegal

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 182
Author(s):  
Moustapha Kebe ◽  
Alassane Traore ◽  
Manousos Ioannis Manousakas ◽  
Vasiliki Vasilatou ◽  
Ababacar Sadikhe Ndao ◽  
...  

Identifying the particulate matter (PM) sources is an essential step to assess PM effects on human health and understand PM’s behavior in a specific environment. Information about the composition of the organic or/and inorganic fraction of PM is usually used for source apportionment studies. In this study that took place in Dakar, Senegal, the identification of the sources of two PM fractions was performed by utilizing data on the elemental composition and elemental carbon content. Four PM sources were identified using positive matrix factorization (PMF): Industrial emissions, mineral dust, traffic emissions, and sea salt/secondary sulfates. To assess the effect of PM on human health the air quality index (AQI) was estimated. The highest values of AQI are approximately 497 and 488, in Yoff and Hlm, respectively. The spatial location of the sources was investigated using potential source contribution function (PSCF). PSCF plots revealed the high effect of transported dust from the desert regions to PM concentration in the sampling site. To the best of our knowledge, this is the first source apportionment study on PM fractions published for Dakar, Senegal.

Author(s):  
M. Pandey ◽  
V. Singh ◽  
R. C. Vaishya

Air quality is an important subject of relevance in the context of present times because air is the prime resource for sustenance of life especially human health position. Then with the aid of vast sums of data about ambient air quality is generated to know the character of air environment by utilizing technological advancements to know how well or bad the air is. This report supplies a reliable method in assessing the Air Quality Index (AQI) by using fuzzy logic. The fuzzy logic model is designed to predict Air Quality Index (AQI) that report monthly air qualities. With the aid of air quality index we can evaluate the condition of the environment of that area suitability regarding human health position. For appraisal of human health status in industrial area, utilizing information from health survey questionnaire for obtaining a respiratory risk map by applying IDW and Gettis Statistical Techniques. Gettis Statistical Techniques identifies different spatial clustering patterns like hot spots, high risk and cold spots over the entire work area with statistical significance.


2015 ◽  
Vol 15 (17) ◽  
pp. 23989-24039 ◽  
Author(s):  
F. Amato ◽  
A. Alastuey ◽  
A. Karanasiou ◽  
F. Lucarelli ◽  
S. Nava ◽  
...  

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 %).


2019 ◽  
Vol 7 (3) ◽  
pp. 961-966
Author(s):  
Harshita Raj ◽  
Suhasini Vijaykumar

Urban Climate ◽  
2021 ◽  
Vol 36 ◽  
pp. 100789
Author(s):  
Niladri Das ◽  
Subhasish Sutradhar ◽  
Ranajit Ghosh ◽  
Prolay Mondal

2021 ◽  
Vol 1058 (1) ◽  
pp. 012014
Author(s):  
Ruqayah Ali Grmasha ◽  
Shahla N. A. Al-Azzawi ◽  
Osamah J. Al-sareji ◽  
Talal Alardhi ◽  
Mawada Abdellatif ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document