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Variation of PM2.5 Concentrations in Shopping Malls in Autumn, Changsha

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Jinhua Hu
Jinhua Hu
Jinhua Hu
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Nianping Li
Nianping Li
Nianping Li
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Abstract

Good indoor air quality is very essential to shoppers. In order to characterize the indoor air quality in shopping malls, a typical shopping mall in Changsha city was selected for this study. Indoor/outdoor PM2.5 concentrations and meteorology parameters were monitored in autumn, 2014. Diurnal variation of PM2.5 concentration in both indoor different functional areas and outdoor air were discussed. Outdoor average PM2.5 concentration at any time was higher than that of indoor. Indoor average PM2.5 concentration on weekend were greater than that of weekday. Among different functional areas, the average PM2.5 concentration of cosmetics area was the highest, followed by dining area, public walkway, clothes area and shoes& bags area. Moreover, the impacts of meteorology parameters on indoor PM2.5 concentrations were analyzed. Temperature (R2=0.354) and wind speed (R2=0.285) showed moderate dependence with indoor PM2.5 concentrations. However, relative humidity (R2=0.075) and barometric pressure (R2=0.047) showed weak correlation.
Available via license: CC BY-NC-ND 4.0
Content may be subject to copyright.
Procedia Engineering 121 ( 2015 ) 692 – 698
1877-7058 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the organizing committee of ISHVAC-COBEE 2015
doi: 10.1016/j.proeng.2015.09.006
ScienceDirect
Available online at www.sciencedirect.com
9th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC) and the 3rd
International Conference on Building Energy and Environment (COBEE)
Variation of PM2.5 Concentrations in Shopping Malls in Autumn,
Changsha
Jinhua Hu, Nianping Li
*
College of Civil Engineering, Hunan University, Changsha 410082, China
Abstract
Good indoor air quality is very essential to shoppers. In order to characterize the indoor air quality in shopping malls, a typical
shopping mall in Changsha city was selected for this study. Indoor/outdoor PM2.5 concentrations and meteorology parameters
were monitored in autumn, 2014. Diurnal variation of PM2.5 concentration in both indoor different functional areas and outdoor
air were discussed. Outdoor average PM2.5 concentration at any time was higher than that of indoor. Indoor average PM2.5
concentration on weekend were greater than that of weekday. Among different functional areas, the average PM2.5 concentration
of cosmetics area was the highest, followed by dining area, public walkway, clothes area and shoes& bags area. Moreover , the
impacts of meteorology parameters on indoor PM2.5 concentrations were analyzed. Temperature˄R2=0.354˅ and wind speed
˄R2=0.285 ˅showed moderate dependence with indoor PM2.5 concentrations. However, relative humidity(R2=0.075) and
barometric pressure˄R2=0.047˅showed weak correlation.
© 2015 The Authors.Published by Elsevier Ltd.
Peer-review under responsibility of the organizing committee of ISHVACCOBEE 2015.
Keywords: Indoor air quality; PM2.5; Shopping mall; Meteorology parameters; Concentration variations
1. Introduction
With the rapid development of economy in China, more and more large-scale shopping malls or shopping centers
were constructed. The shopping levels have been greatly improved. Services and functional structure of large-scale
* Corresponding author. Tel.: +86-731-88822667; fax: +86-731-88822667.
E-mail address: linianping@126.com
© 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the organizing committee of ISHVAC-COBEE 2015
693
Jinhua Hu and Nianping Li / Procedia Engineering 121 ( 2015 ) 692 – 698
shopping malls have been fully developed from entertaining, oversized or upscale to integrate entertainment, dining,
shopping to one-stop service features to pursue for safe, comfortable and pleasant shopping.
In recent years, fog and haze weather appeared frequently in China. PM2.5 concentrations exceeded the limit
values shown in the instrument across the country. Changsha city in Hunan province was shrouded by continuous
fog and haze weather inevitably especially in autumn or winter. PM2.5 ˄particles with aerodynamic diameter<
2.5μm˅which was with small size(compared to PM10) and rich in a lot of toxic and hazardous substances has been
concerned with various adverse effects on human health, indoor air quality, urban visibility and climate[1,2]. A lot of
studies showed that morbidity and mortality of human disease has strong correlation with atmospheric PM pollution
[3,4,5]. In a large cohort study with a follow-up period of 16 years by American Cancer Society Study, each
additional 10μg/m3 of PM2.5 concentration led to an increase of cardiovascular mortality by 8-18%[6].
In shopping malls, customers spend their time ranging from a few minutes to a few hours while the shop
assistants spend 8 hours even longer. Paying close attention to the customer’s comfort has become the focus chase of
business people in constant pursuit of profit. Creating a healthy and comfortable shopping environment is not only to
protect public health, but also help to improve the economic effects of business. Therefore, it is essential to reveal
the fine particulate matter pollution inside and outside the shopping malls to protect the health of shoppers and
workers.
2. Methods
2.1. Current research progress of indoor air quality in shopping malls
Shopping malls are the general name of public buildings that provide trading places for the production and supply
of various commodities for social life[7]. Personnel density of shopping malls is often far more than residential and
office buildings. Carbon dioxide and body odor which were produced by the human body, dust and bacteria when
shoppers entered the mall and the goods, various pollutants emitted by decorative materials couldn’t be effectively
diluted because of the unreasonable air conditioning system. It can easily lead to deterioration of indoor air quality
and other related issues which were bad for the health of customers and staff. It was even more serious when on
weekends.
Li[8] selected nine shopping mall in Hong Kong for indoor air quality study. CO2, CO, PM10, HCHO, THL were
collected on both weekdays and weekends. It was found that more than 40% of the shopping malls had 1-h average
CO2 levels above the 1000 ppm of the ASHRAE standard and the highest indoor PM10 level at a mall was 380μg/m3
which was exceeded the Hong Kong Indoor Air Quality Objective. Yan[9] detected the concentrations of PM10 and
PM2.5 inside and outside of ten shopping centres in Xi’an. The results showed that the PM concentrations exceeded
the relevant standard in different degrees. Wen [10] thought shoppers density and indoor cooking activities inside a
shopping mall effected the indoor PM levels obviously by collecting PM10, PM2.5 and PM1 concentrations in three
shopping malls in Shanghai. It was found that there exist problems of large difference in temperature between
storeys of the building and insufficient ventilation rate in Shanghai [11]. Jang[12] showed that there existed great
discrepancies between work staff and customers about subjective evaluations on indoor environment.
Obviously, most present investigations focused on subjective investigations for 1990s shopping malls or
emphasized the measurements of the indoor temperature, the relative humidity, CO2, PM10 or other parameters.
Therefore, it is necessary to have in-depth investigation of PM2.5 concentration in functional areas of shopping malls
which was built around or after the yeas of 2005. A typical large-scale shopping mall located in the downtown of
Changsha city was selected for the study. The PM2.5 concentrations among different functional areas were collected
on both weekend and weekday.
2.2. Field study
The selected shopping mall is from B1 to 9F with over 60,000 square meters and with shopping, dining,
entertainment and leisure areas. A centralized ventilation and air conditioning system was designed for the building.
During the field study, there were no smokers and the air conditioning system has been running. Typical areas such
as shoes&bags area, clothes area, dining area and cosmetics area were selected as sampling sites of PM2.5
694 Jinhua Hu and Nianping Li / Procedia Engineering 121 ( 2015 ) 692 – 698
concentrations. General characteristics of sites in building were shown in Table1. SIDEPAK AM510 Aerosol
monitor (TSI Incorporated, USA) with a 2.5μm inlet was adopt to measure the real-time PM2.5 concentrations of air.
The meteorological parameters such as temperature, relative humidity and wind speed were collected by
VELOCICALC 8347(TSI Incorporated, USA). Aneroid barometer (China) was used to obtain atmospheric pressure
values.
Table 1. General characteristics of sites
Site No.
No. of floors
Site feature
S1
B1
Women sh oes area
S2
B1
Cosmetic area
S3
B1
Dining area
S4
B1
Public walkway
S5
5
Men shoes area
S6
5
Men bags area
S7
5
Clothes area
S8
5
Public walkway
S9
8
Children's Playground
S10
8
Children's clothing area
S11
8
Dining area
S12
8
Public walkway
O
1
Outdoorair
The survey was conducted during October 12~13, 2014 from 10:00 to 22:00 with 90 minutes as a time interval.
Each measurement time was set to 3 min. The height of measurement site was between 1.2~1.5m. The shopping
mall was with 9 floors in total and B1, 5F and 8F were selected as measurement floors. Four sites were measured in
each floor. The data of outdoor site was the average data of east, south, west and north. Filed measurement
appearances were shown in Fig.1.
(a) Shoes area test site (b) Clothes area test site (c) Dining area test site
Fig. 1. Appearance of measurement for PM2.5
3. Results and discussion
3.1. Spatial variation
The average PM2.5 concentration in B1, 5F and 8F were presented in Fig.2. The maximum average PM2.5
concentration was found in B1, followed by 8F and 5F. The maximum PM2.5 concentration in B1 was seen during
13:00~14:30(217μg/m3), whereas the minimum value was found during 17:30~19:00˄165μg/m3˅. The maximum
and minimum PM2.5 concentration of 8F and 5F were during 11:30~13:00 and 20:30~22:00 respectively. It was
695
Jinhua Hu and Nianping Li / Procedia Engineering 121 ( 2015 ) 692 – 698
observed that B1 was with cosmetics area and an open dining area and a large number of shopping customers come
to have lunch or dinner. This was bound to affect the overall PM2.5 concentration in the layer. The dining area in 8F
showed a poor impact on other functional areas because of a spatial interval. Men shoes, bags and clothes were set in
5F where shopping customers were less than in other areas to some extent. Suspended particulate matter caused by
human activities was relatively poor, and the average PM2.5 concentration in 5F was the lowest.
Fig. 2. Average PM2.5 concentration in B1, 5F and 8F Fig. 3. Average PM2.5 concentration in different functional areas
Fig.3 showed that all functional areas except public walkway were with maximum PM2.5 concentration during
11:30~13:00. The PM2.5 concentration of cosmetics area was found to be the highest(244μg/m3) followed by dining
area(199μg/m3), public walkway(160μg/m3), clothes area(146μg/m3) and shoes&bags area(139μg/m3). These
variation characteristics were closely related to the objects activities in different areas. Female customers flocked to
the cosmetic area especially on weekend. Human activities such as sitting and walking might affect migration and
movement of fine particulate matter. Cooking activities in dining area would produce a lot of smoke which caused
direct contamination of indoor environment. Since there were no obvious sources of pollution and a relatively less
number of shopping customers in clothes area and shoes&bags, the annual PM2.5 concentration was lower.
3.2. Variation of average indoor/outdoor PM2.5 concentration
The average indoor/outdoor PM2.5 concentrations were showed in Fig.4. The outdoor average PM2.5
concentrations were higher than that of indoors with an average deviation of 160±19μg/m3.The minimum outdoor
and indoor annual average PM2.5 concentration ˄302μg/m3 and157μg/m3,respectively ˅were found during
17:30~19:00. The maximum outdoor average PM2.5 concentration (383μg/m3) was found during 11:30~13:00.
However, the maximum indoor average PM2.5 concentration (180μg/m3) was seen during 13:00~14:30. Usually, 8:00,
12:00 and 18:00 were the rush hour with heavy vehicle exhaust. However, concentration lag phenomenon was found
because there was a certain distance away from the artery of communications[13]. These showed that the indoor
particle concentration was mainly influenced by outside sources.
696 Jinhua Hu and Nianping Li / Procedia Engineering 121 ( 2015 ) 692 – 698
Fig. 4. Average indoor/outdoor PM2.5 concentration
3.3. Variation during weekday and weekend
Fig.5 and Fig.6 showed outdoor and indoor PM2.5 concentrations during weekday and weekend. Outdoor and
indoor PM2.5 concentrations during weekend were substantially higher than the corresponding levels during weekday
with a mean deviation 134±55μg/m3 and 165±9μg/m3 respectively. It was concluded that PM2. 5 concentrations
during weekend were higher than the corresponding levels during weekday in spite of indoor or outdoor. This was
mainly because elevated indoor PM2.5 concentrations during weekend was predominantly generated by the activities
such as movement of shopping occupants. Austen[14] pointed out that the human body was an important source of
particles with particle (greater than 0.3μm) incidence of 105/min at rest and 2.5×106/min with completing standing
up and sitting down movements and the number of particles would be greatly increased when walking.
Fig. 5. Outdoor PM2.5 concentrations during weekday and weekend Fig.6. Indoor PM2.5 concentrations during weekday and weekend
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Jinhua Hu and Nianping Li / Procedia Engineering 121 ( 2015 ) 692 – 698
3.4. Indoor/Outdoor PM2.5 ratios
Indoor to outdoor PM2.5 concentration ratio(I/O) was often used to describe the temporal dynamics between
indoor and outdoor air pollution of all the measurement sites[15,16]. If I/O ratio was less than 1, it indicated that the
indoor PM was mainly affected by outdoor particles. If not, indoor PM was mainly caused by the interior
sources[17,18]. I/O ratio vales of all sites were within 0.46 to 0.52.This suggests that indoor particulate matter was
mainly influenced by outdoor particulate matter which was affected by traffic dust and the impact of nearby
constructions. Therefore, indoor and outdoor PM2.5 concentrations values in the field measurement were rather lager.
3.5. Impact of meteorology on PM2.5 concentration
The PM concentration was significantly affected by the distribution and intensity of pollution sources and
meteorological factors. On the basis of constant pollution sources, the meteorological conditions were the main
factors influencing the concentration of pollutants in the atmosphere[19,20]. It was found that ambient
temperature(R2˙0.354), wind speed(R2˙0.285) and PM2.5 concentration were moderately correlated. However,
relative humidity(R2˙0.075), barometric pressure(R2˙0.047) and PM2.5 concentration were poorly correlated.
These were consistent with the researches of Chan [21] and Chithra and Shiva Nagendra [22]˄R2˙0.0237ˈ
0.192˅.
4. Conclusions
(1)During the sampling period of shopping mall in autumn, the outdoor PM2. 5 concentrations were greater than
that of indoor and PM2.5 concentrations on weekend were greater than that on weekday. The maximum PM2.5
concentrations both indoor and outdoor were during the period 11:30~14:30 whereas the minimum values were
found during 17:30~19:00.This was the combined effect of local meteorological characteristics and indoor activities.
(2)During different functional areas, the average PM2.5 concentration of cosmetics area was the highest, followed
by dining area, public walkway, clothes area and shoes& bags area. These was closely related to objects activities in
different areas.
(3)Correlation analysis between meteorology parameters and PM2.5 concentration showed that ambient
temperature, wind speed and PM2.5 concentration were moderately correlated. Relative humidity, barometric
pressure and PM2.5 concentration were weakly correlated. Pollutants were usually subject to the effects of a variety
of factors combined with atmospheric motions.
(4) Indoor fine particulate matter pollution was mainly influenced by the combined effect of indoor human
activities and outdoor sources. The indoor and outdoor PM2.5 mass concentrations were obtained in this study were
exceed than the standard established by China [23]. It is necessary to control the emission of pollutants and reduce
the impact of outdoor air quality to indoor air quality .
Acknowledgements
This study was sponsored by the National Natural Science Foundation of China( No. 5157080886). The authors
would like to thank the staffs of the shopping mall who were involved in this study for their helpful cooperation and
the students in Hunan University who helped in the field measurement.
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Full-text available
  • Oct 2021
Shopping malls in Hong Kong are usually located near major roads. Indoor air quality (IAQ) in these buildings is subject to infiltration of outdoor traffic-related pollutants, such as PM10, PM2.5, CO, and NO2. Furthermore, the existence of indoor sources and building geometry added to the complexity of variations in IAQ. To understand outdoor infiltration and spatial heterogeneity of these pollutants, we conducted fixed and cruise indoor sampling, together with simultaneous outdoor measurements, in a typical mall in Hong Kong. The cruise sampling was conducted indoors on a predesigned route and repeated 15 times. Outdoor infiltration was quantified based on regression analysis between indoor and outdoor sampling. Results showed that 75% of PM2.5, 53% of PM10, and 59% of NO2 were infiltrated into the mall during opening hours. Elevated PM2.5 and CO were observed during the dinner period, suggesting an impact from cooking. Substantial spatial variations were observed for PM10, PM2.5, and NO2, particularly at locations near entrances and restaurants. Measures are needed to reduce pollution intrusion from building openings and cooking-related sources to improve air quality in the selected mall. Fixed and cruise sampling methods used in this study provide insights on sensor deployment for future air quality monitoring in buildings.
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... Data capture of real-world phenomena is a key element of research on the built environment, from particulate matter concentrations in indoor spaces (Hu & Li, 2015), to natural ventilation behaviour (Alfata et al., 2015;Karava et al., 2011;Zakaria et al., 2015), and comparisons of weather station data to local conditions (e.g. Omrani et al., 2016). ...
Humans in the Loop: Incorporating Occupant Behaviour in Building Performance Simulation
Thesis
Full-text available
  • Aug 2020
In this thesis I argue for the need to consider the effects of occupant behaviour on the environmental sustainability of buildings, and propose and test novel methods to incorporate this behaviour in building performance simulations during the architectural design process. Due to rising energy prices and the increasing adverse effects of carbon emissions on the environment, there is a growing need to reduce energy consumption through sustainable building design. At the same time, there are rising expectations for human comfort in indoor environments. There are two approaches to addressing these problems: either to design increasingly hermetic buildings with automated HVAC systems, or to introduce low-tech solutions and create a higher customizability for occupants to control their personal microclimates (through operable windows, blinds, fans, thermostats, etc.). In this research, I explore the latter. Sustainable building design often employs digital simulations, and while current simulation software can be used to accurately model deterministic physical systems, it only offers limited capacity to model human factors. Human behaviour is often misrepresented, leading to large discrepancies between simulation and reality. This has caused the emergence of a recent field of research aimed at measuring and creating probabilistic models of occupants’ climate-adaptive behaviours. Research on this topic has so far been limited to building science, meaning that the researchers neither produce tools that embed well in the architectural design workflow, nor do they make specific suggestions as to how their insights may translate into architectural decision-making. The contribution of my research is twofold: firstly, I develop a digital toolkit prototype that incorporates some of these models, enabling architects and engineers to simulate the impacts of several human factors on building performance. Secondly, I demonstrate the possible design implications of behaviour modelling in a series of design studies. I analyse the proposed methods along four metrics: accuracy, usability, applicability and design impact. The accuracy of one model within the toolkit prototype was cross-validated using field study data collected in an operational building; in this case, the stochastic modelling approach achieved a substantially higher accuracy than the conventional deterministic approach. The advantages and limitations of using the toolkit prototype are discussed through heuristic methods adopted from the field of software engineering. Applicability is discussed through an interview with industry professionals. In the digital studies, I analyse the impact of occupant behaviour modelling by comparing the conventional versus stochastic models within several design contexts. The choice of occupant model had a large effect on simulation results, from which I extrapolate implications for specific architectural design decisions. While generalisations on the superiority of stochastic over deterministic occupant modelling must be based on more widespread validation, the fact that the choice of modelling approach can lead to different architectural outcomes should alert architects to the fact that occupants have a large impact on building microclimates. Occupant behaviour should therefore be considered in sustainable design.
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Relationship between meteorological conditions and atmospheric PM2.5 in uranium mining areas and source analysis
Article
  • Feb 2024
In this study, the relationship between PM2.5 and ambient temperature, humidity, air pressure, wind speed and air indices was investigated by combining specific facilities at uranium mine with villages and cities. Humidity, atmospheric pressure and air quality index were positively correlated with PM2.5 concentration, whereas temperature and wind speed were negatively correlated, while altitude had no correlation with PM2.5 concentration. By constructing the corresponding PMF model data analysis, this study obtained six sources of eight metallic elements (U, Th, Al, Cu, Pb, Cd, Ti, Zn) in PM2.5 around a decommissioned uranium mine.
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Relationship between meteorological conditions and atmospheric PM2.5 in a uranium mining area and analysis of pollution sources
Preprint
Full-text available
  • Oct 2022
Combining the special facilities of uranium mine with villages and towns, this paper analyzed the correlation between PM 2.5 and atmospheric temperature, humidity, air pressure, wind speed and air index analyzed by PM 2.5 . Humidity, atmospheric pressure and air quality index were positively correlated with PM 2.5 concentration, while temperature and wind speed were negatively correlated with PM 2.5 concentration, while altitude had no correlation with PM 2.5 concentration. The seasonal variation of PM 2.5 concentration in this area was as follows: winter (31.5 ㎍/m ³ ) > spring (25.78 ㎍/m ³ ) > autumn (15.59 ㎍/m ³ ) > summer (10.61 ㎍/m ³ ). PMF model (orthogonal matrix factor analysis) was used to analyze various pollution sources. It was found that the contribution of various pollution sources was soil source (33.7%), combustion source (19.2%), traffic source (18.2%), industrial source (15.0%) and natural source (3.3%).
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Ventilative Cooling and Air Pollutants
Chapter
  • Jun 2021
The majority of office and other non‑domestic buildings use mechanical cooling and ventilation, even when an optimized natural ventilation (NV) system could meet cooling and fresh air requirements. However, in most large cities, the outdoor environment is contaminated with a combination of noise, fine particles, heat and toxic gases. This contaminated environment has a detrimental impact on naturally ventilated buildings due to their lack of filtration and outdoor noise attenuation systems. This chapter presents a numerical analysis of the effect of fine particle pollution (PM2.5) on the NV potential of office buildings in California, Europe and Asia. Several years of measured weather and PM2.5 concentration data were used to perform dynamic thermal and airflow simulation analysis. Detailed simulation results show that a hybrid NV system can reduce the air‑conditioning and ventilation electricity consumption of a well‑designed office building by up to 83% (which can be increased to up to 93% by the availability of personal comfort systems), in comparison to an office using, during all working hours, a mechanical cooling and ventilation system equipped with a high‑efficiency particle filter. Unfortunately, in this hybrid approach, high levels of outdoor PM2.5 penetrate the indoor environment, increasing occupant cumulative exposure by up to six times. To overcome this problem, two exposure control approaches were tested. Using NV only during moments of low outdoor PM2.5 concentrations limits the exposure increase to up to three times but at the cost of reducing energy savings. Equipping NV openings with an electrostatic filter would result in a similar exposure reduction, but at a very low energy cost, taking full advantage of NV’s saving potential.
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Characteristics of indoor and outdoor particle concentrations in air conditioning rooms in summer, Shanghai
Article
  • Dec 2006
The time series of indoor/outdoor PM10, PM2.5 and number concentrations are monitored in two types of air-conditioning rooms in Shanghai City in summer. The relationship of PM10, PM2.5, number concentrations and ratio of indoor to outdoor between indoor and outdoor of the rooms, which are equipped with wall-mounting air conditioner and central air-conditioner, are analyzed and discussed, respectively. The testing data show that inner aerosol emission is the main source of wall-mounting air-conditioned rooms, but in central air-conditioning systems, indoor PM2.5 levels are primarily affected by the ambient aerosol concentrations.
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Indoor–outdoor relationship of particulate matter and nitrogen oxides under different outdoor meteorological conditions
Article
  • Mar 2002
  • ATMOS ENVIRON
Respirable suspended particulate matter and nitrogen oxides concentrations were measured inside and outside a student office in an urban location during a 9-month period. Direct reading tapered-element oscillating microbalance (TEOM) instruments and passive sampling techniques were used to provide indoor and outdoor hourly averages of the two pollutants during the sampling period. The variations and correlations of the pollutant concentrations and the indoor–outdoor (IO) ratio against various outdoor meteorological factors, namely temperature, humidity, pressure, wind speed and solar irradiation were studied. Using a data-mining procedure, suitable sets of data were amassed and grouped together to study the effect of these individual weather parameters on the IO ratio. It is found through statistical regression techniques that temperature, humidity and solar irradiation play a vital role in the variation of the IO ratio. In fact, the IO ratio shows convincing tendency of increase with increase of these three weather parameters. On the other hand, both pressure and wind speed seems to have relatively little effect on the IO ratio.
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Optical and thermal measurements of black carbon aerosol content in different environments: Variation of the specific attenuation cross-section, sigma (??)
Article
  • Jun 1993
  • Atmos Environ Gen Top
In optical analysers devoted to the analysis of atmospheric black carbon concentration, the specific attenuation cross-section, σ is the factor used to convert the attenuation of a light beam due to the absorption of aerosols deposited on a filter into their black carbon content.We have tried to gain further insight for a suitable choice of sigma value, using both optical analysis (with an aethalometer) and thermal analysis of black carbon aerosols and comparison of the two sets of results. Samples which were investigated originate from varying environments, including suburban areas, tropical areas where biomass burning was prevalent and from more remote locations. In a given type of atmospheric environment, σ values are found to be constant. However, σ displays an important variability (range: 5–20 m2 g−1) which may be related to the variability of the aerosol mix (internal or external mixture) and the aging of the atmospheric particulate phase.Our results quote unambiguously the need for a modulated calibration of optical analysers depending on the type of atmospheric environments which are studied. They suggest the need to reconsider carefully the black carbon data obtained at remote locations.
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Measurements of PM10 and PM2.5 particle concentrations in Athens, Greece
Article
  • Feb 2003
  • ATMOS ENVIRON
During the period of 1 June 1999 through 31 May 2000, PM2.5 and PM10 concentrations were measured in downtown Athens, Greece. The monitoring site was located 6.7 m above ground near a highly trafficked and congested road. Daily PM10 and PM2.5 samples were collected using semi-automatic low-volume samplers. The average 24-h PM10 concentration during the sampling period was 75.5 μg/m3. Daily PM10 concentrations exceeded the European Union limit (for the year 2000) for approximately 42% of the sampling days. The average PM2.5 measured concentration was 40.2 μg/m3, which is considerably higher than the United States National Ambient Air Quality PM2.5 annual standard, 15 μg/m3. The mean coarse particle concentration, PM10 minus PM2.5, was 35.3 μg/m3 representing about 50% of the average PM10.PM2.5 and PM10 concentrations were highly correlated with carbon monoxide, black carbon and nitrogen oxides and inversely correlated with local wind speed. Regression models using continuous and categorical variables were developed to investigate the complex relationships between meteorological and time period parameters and particle concentrations. The results of this analysis underlined the importance of local emission sources, mostly from traffic, which are responsible for the high PM10 and PM2.5 concentration levels observed during this one year-sampling period.
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