ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-11165-2015Evaluation of regional background particulate matter concentration
based on vertical distribution characteristicsHanS.ZhangY.zhafox@126.comWuJ.ZhangX.TianY.WangY.DingJ.YanW.BiX.ShiG.CaiZ.YaoQ.HuangH.FengY.fengyc@nankai.edu.cnState Environmental Protection Key Laboratory of Urban Ambient Air
Particulate Matter Pollution Prevention and Control, College of
Environmental Science and Engineering, Nankai University, Tianjin, 300071,
ChinaResearch Institute of Meteorological Science, Tianjin, 300074, ChinaY. Zhang (zhafox@126.com) and Y. Feng (fengyc@nankai.edu.cn)7October20151519111651117727January201527May201515September201518September2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/11165/2015/acp-15-11165-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/11165/2015/acp-15-11165-2015.pdf
Heavy regional particulate matter (PM) pollution in China has resulted in an
important and urgent need for joint control actions among cities. It is
advisable to improve the understanding of the regional background concentration
of PM for the development of efficient and effective joint control policies.
With the increase of height the influence of source emission on local air
quality decreases with altitude, but the characteristics of regional
pollution gradually become obvious. A method to estimate regional background
PM concentration is proposed in this paper, based on the vertical
characteristics of periodic variation in the atmospheric boundary layer
structure and particle mass concentration, as well as the vertical
distribution of particle size, chemical composition and pollution source
apportionment. According to the method, the averaged regional background
PM2.5 concentration in July, August and September 2009, being extracted
from the original time series in Tianjin, was 40 ± 20, 64 ± 17
and 53 ± 11 µg m-3, respectively.
Introduction
Atmospheric particulate matter (PM) has attracted considerable attention because
it has been associated with many urban environmental problems, such as acid
precipitation, decreasing visibility and climate change (Zeng and Hopke,
1989; Charlson et al., 1992; Schwartz et al., 1996; Chameides et al., 1999).
PM has also been implicated in human mortality and morbidity (Dockery et al.,
1993; Tie et al., 2009; Lagudu et al., 2011). Among the various sizes of
atmospheric PM, PM2.5 (PM with aerodynamic diameter less than
2.5 µm) is considered to be of great significance due to its links
to human respiratory health (Englert, 2004), regional-scale air pollution
(Husar et al., 1981; Chameides et al., 1999), and potential acid rain
enhancement (Cao et al., 2013).
The combination of rapid industrialization and urbanization has resulted in
considerable environmental problems throughout China, especially in the
clusters of cities (Shao et al., 2006). The coexistence of numerous air
pollutants with high concentrations and the complicated interactions among
them leads to the formation of an air pollution complex (Shao et al., 2006;
Zhu et al., 2011). One of the major pollutants is PM (Tie et al., 2006; Liu
et al., 2011; Chen et al., 2012; Han et al., 2013). The origin of PM is
complex. It involves both primary emissions as well as secondary particle
production due to chemical reactions in the atmosphere (Shi et al., 2011;
Tian et al., 2013; Hu et al., 2013; Guo et al., 2013). With a lifetime of
days to weeks in the lower atmosphere, PM2.5 can be transported
thousands of kilometres (Hagler et al., 2006). The trans-boundary transport
of PM2.5 and the gaseous precursors has significant influence on the
regional background PM level in the cluster of cities. In order to study the
regional-scale PM pollution and develop efficient joint control policies,
it is necessary to improve understanding of regional background PM
concentration.
Background concentration has been defined as concentration observed at a site
“that is not affected by local sources of pollution” (WHO, 1980; Menichini
et al., 2007). McKendry et al. (2006) defined background concentration as one of “those
pollutants arising from local natural processes together with those
transported into an airshed from afar (the latter may be either natural or
anthropogenic in origin)”. Background concentration in this paper is defined
to include collective contributions from regional anthropogenic and natural
emissions and long-range transport.
Background concentrations are not constant because of meteorological
variability, complexity of chemical reactions, as well as spatially and
temporally varying emissions. Regional-scale PM pollution is associated with
synoptic scenarios that induce the transfer, accumulation and the formation
of pollutants at regional scales. Simply taking measurements at local scales is not well
suited to adequately investigate the regional background concentration. There
is always the possibility that the “air quality background monitoring
station” is directly influenced by local emission sources and thus not truly
representative of the background level (Tchepel et al., 2010). That is to
say, background concentration can hardly be measured directly, so it is
critical to choose representative and appropriate values. Usually, by setting
some restrictions to identify and remove the influence of local pollution,
background concentration can be determined indirectly. There are several
studies mentioning the methods for determining the background concentration.
These methods can be classified into four categories. (1) The physical methods
identify the regional pollution process and local pollution process via
synoptic situation, duration of the synoptic system, consistency of vertical
wind, atmospheric stability, particle size distribution, etc., and then
the data of the “background period” influenced by regional processes are
selected (Pérez et al., 2008). (2) The chemical methods identify the
regional process according to chemical composition in PM and synchronous
observation of other pollutants, and then remove the data influenced by local
processes (Menichini et al.,
2007). (3) The statistical methods use discriminant analysis, cluster
analysis and principal component analysis (PCA) to identify the data that
characterize the regional background PM (Langford et al., 2009; Tchepel et
al., 2010). (4) Numerical simulation methods use trajectory models and
atmospheric dynamics–chemical coupled models to simulate the regional
background pollution (Dreyer and Ebinghaus, 2009; Tchepel et al., 2010).
With the increase of height, the influence of source emission on local air
quality decreases with altitude, but the characteristics of regional
pollution gradually become obvious. Influenced by atmospheric dynamics and
thermal effects, meteorological variables and pollutant measurements at
different heights within the boundary layer could represent different
horizontal scales of pollution. Sites at near-ground height (5–10 m) are
influenced extensively by human activities, and the data observed at these
sites could represent the street scale. Impacts from local disturbance
weaken with height gradually and observations at greater heights could
represent larger horizontal scales. When the height increases to the top of
the urban atmospheric boundary layer, observations can represent urban
scales. Heights above the urban boundary layer could to some extent reflect
the characteristics of regional scales. A tall tower is commonly used in
observation of boundary layer meteorological, micrometeorological and
atmospheric chemical variables, e.g. vertical profile and fluxes
(Heintzenberg et al., 2008, 2013; Brown et al., 2013; Andreae et al., 2015).
The footprint concept is capable of linking observed data collected at the
different height levels of the tower to spatial context. The integral beneath the
footprint function expresses the total surface influence on the signal
measured by the sensor at a height above the surface (Schmid, 2002; Ding et
al., 2005; Foken and Nappo,
2008). Three main factors affect the size and shape of flux footprint:
increase in measurement height, decrease in surface roughness, and change in
atmospheric stability from unstable to stable leading to an increase in
size of the footprint (https://en.wikipedia.org/wiki/Flux_footprint).
Combined information from meteorological data and simultaneous aerosol
measurements at the different levels of the tower have made it possible to gain
insights into the transport of aerosols and their vertical distributions which strongly
depend on meteorological conditions, boundary layer dynamics and
physiochemical processes (Guinot et al., 2006; Pal et al., 2014). In this
paper, the periodic variation in the atmospheric boundary layer structure and
PM mass concentrations, as well as the vertical distribution characteristics
of particle size, chemical composition and pollution sources, were studied to
characterize the regional pollution contribution. On this basis, the
height above which there is relatively less influence by local pollution emission can
be determined and the regional background PM concentration can be extracted
from the observation data and estimated by mathematical methods.
Data sources and treatmentObservation site
The data used in this study were collected at a 255 m meteorological tower
which is located at the atmospheric boundary layer observation station (WMO
Id. No. 54517, 39∘04′29.4′′ N, 117∘12′20.1′′ E) in
Tianjin, China, where there is a residential and traffic mixing area. There are no
industrial pollution sources near the site. Tianjin is adjacent to the BoHai
Sea and situated in the eastern part of the Beijing–Tianjin–Hebei area, one
of the most heavily polluted areas in China. Tianjin covers an area of
11 300 km2 and has a population of 8 million. Due to rapid
industrialization and urbanization in recent years, air pollution has become
a serious problem in this city.
Observation method and data treatment
Horizontal wind speed, wind direction, and temperature were measured at 15
platforms (5,10, 20, 30, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220 and
250 m) every 10 s and averaged hourly. Three-dimensional ultrasonic
anemometers (CAST-3D) were mounted at 40, 120 and 220 m to measure the
turbulent fluxes. Hourly meteorological data (WMO Id. No. 54517) in the year
of 2009 were used in this paper.
Mass concentrations of PM2.5 were measured using ambient particulate
monitor chemiluminescence (TEOMR-RP1400a) at four different heights (2, 40,
120, and 220 m) from 1 July to 30 September 2009. The monitor's data output
consists of 1 and 24 h average mass concentration updated every 10 min and
on the hour, with the precision of ±1.5 µg m-3 (1 h avg)
and ±0.5 µg m-3 (24 h avg) respectively. The accuracy of
mass measurement is ±0.75 %.
In order to study the vertical characteristics of PM chemical composition and
sources, 24 h PM10 filter samples were collected from local Beijing
time 08:00 to 07:00 GMT + 8:00 the next day using medium-volume
PM10 samplers (TH-150, Wuhan Tianhong Intelligence Instrumentation
Facility) at the heights of 10, 40, 120, and 220 m from 24 August to
12 September 2009. The sampler has a system of automatic constant-flow
control. Flow rate of sampling in this study is 100 L min-1, and the
relative error of flow is less than 3 %. At each height, PM10 filter
samplings were equipped with two samplers in parallel: one is for chemical
analysis of inorganic composition on polypropylene filters (90 mm in
diameter, Beijing Synthetic Fiber Research Institute, China) and the other is
for organic composition analyses on quartz-fibre filters (90 mm in diameter,
2500QAT-UP, Pall Life Sciences).
Before and after sampling, filters were conditioned for 48 h in darkened
desiccators prior to gravimetric determination. The filters were weighed on a
electronic microbalance (AX205, Mettler-Toledo, LLC, with a ±0.01 mg
sensitivity) in a clean room under constant temperature
(20 ± 1∘) and RH (40 ± 3 %). Samples were stored
air-tight in a refrigerator at about 4∘ before chemical analyses.
Elements (Si, Ti, Al, Mn, Ca, Mg, Na, K, Cu, Zn, Pb, Cr, Ni, Co, Fe, and V)
were analysed by Inductively Coupled Plasma atomic emission spectroscopy (ICP
9000 (N+M) Thermo Electron Corporation, USA). Blank filters were processed
simultaneously with sample filters. Ultrapure water, both unfiltered and
filtered, and nitric acid were also analysed. The average element values in
the blanks were subtracted from those obtained for each sample filter;
10 % of total samples were analysed in duplicate to verify sample
homogeneity. The precision and accuracy were checked by analysis of an
intermediate calibration solution. Extraction efficiencies were evaluated by
analysis of the certified reference material from the National Research Center for
CRM. The recovery value was between 85 and 110 %. Calibration check was
performed to ensure a relative error of no more than 2 % for major elements
and 5 % for trace elements.
Water-soluble ions (NH4+, Cl-, NO3-, and SO42-)
were analysed by ion chromatography (DX-120, Dionex Ltd., USA) after
extraction by deionized water. External calibration was employed to quantify
the ion concentrations. A calibration check with external standards was
performed to ensure a relative error of no more than 10 %. The uncertainty
contributions of the calibration curve, calibration solution and repetitive
measurement for unknown sample were taken into account. The expanded
uncertainty was 3.8 % with a coverage factor of k=2.
The thermal optical carbon analyser (Desert Research Institute (DRI) Model
2001, Atmoslytic Inc., Calabasas, CA, USA) was used to measure organic carbon
(OC) and elemental carbon (EC). The heating process can be found in the
IMPROVE_A protocol (Chow et al., 2010, 2011; Cao et al., 2003). Field
blank and lab blank were considered and all sampling concentrations were
revised by blank concentration. The uncertainty contributions of the
calibration curve, calibration solution and repetitive measurement for
unknown sample were taken into account. The expanded uncertainty was
7.6 % with a coverage factor of k=2.
Vertical variation characteristics of urban boundary structureThermal and dynamic characteristics in surface layer
Surface layer has a remarkable effect on the diffusion of air pollutants.
This layer is strongly affected by human behaviour on the ground. Figure 1
presents the diurnal variation of averaged wind speed in four seasons at
different heights in Tianjin. The four seasons were designated as March–May
for spring, June–August for summer, September–November for autumn, and
December–February for winter. Diurnal variation patterns of
wind speed were similar in each season. The wind speed is high in daytime and
low at night below 100 m, whereas there is low wind speed in the daytime and high wind speed at
night above 100 m.
Figure 2 shows the vertical profile of wind speed and temperature in low
atmosphere under different stability. The gradient Richardson number
(Ri) was used for classifying the atmospheric stability conditions:
Ri=gT‾ΔTz1z2lnz2z1+rd×z1z2lnz2z1Δu,
where ΔT=T2-T1, Δu=u2-u1, T2 and
T1 are the measured temperatures at the height of z2 and z1,
T‾ is the averaged temperature in the layer between level z2 and z1, u2 and u1 are the measured wind speed at levels
z2 and z1, g is the gravitational acceleration, and rd is the
dry adiabatic lapse rate. According to the values of Ri, three
different conditions can be distinguished: Ri≥0.1 for stable
condition, -0.1<Ri<0.1 for neutral condition, and Ri≤-0.1 for unstable condition.
The atmospheric layer at 100–150 m is considered as a transition layer, the
variation patterns of temperature and wind speed with height were different
compared with the upper and lower layers. A weak vertical gradient in the
temperature profile was observed over 100 m. Similarly, a small vertical
gradient in wind speed was found over 150 m.
Diurnal variation of averaged wind speed in each season at different
heights.
The height of nocturnal planetary boundary and vertical variation
of turbulent intensity
The height of the planetary boundary layer (PBL), indicating the range of
pollutants diffused by thermal turbulence in the vertical direction (Kim et
al., 2007; Lena and Desiato, 1999), can be calculated by wind and temperature
profiles (Seibert et al., 2000; Han et al., 2009). Based on the temperature
profile observed at the tower, the vertical gradient of temperature was
calculated as
ΔTΔZ=Tz+1-TzZz+1-Zz,
where Tz+1 and Tz represent the measured
temperatures at levels z+1 and z, and Zz+1 and Zz represent the altitudes at levels z+1 and z. The height of the
nocturnal planetary boundary layer (NPBL) is determined by the bottom of the
inversion, i.e. the layer in which the temperature profile has a positive
gradient. As shown in Fig. 3, the seasonal variation of the NPBL height is
generally small, with seasonal averaged NPBL height ranging from 114 to
142 m.
Vertical distribution profile of average wind speed and temperature
in low atmosphere under different stability conditions.
In this study, hourly averaged PM2.5 concentration measurement and
24 h PM10 filter sampling were conducted at four platforms.
The heights of the first and second platform are inside the NPBL, the third platform
is located at the top of the NPBL, and the fourth platform is generally outside
the NPBL. Due to the dynamical stability of the NPBL, air pollutants in the
surface layer are normally trapped inside the NPBL and rarely mix with the
pollutants outside the NPBL. Very different distribution characterizations of
PM were measured inside and outside the NPBL (see Sect. 4).
Based on the observation data from the three-dimensional ultrasonic
anemometers, the turbulent intensity were calculated. As a whole, the
averaged diurnal variations of turbulent intensity in each season (Fig. S1 in
the Supplement) were reflecting the same trends. The diurnal peaks appeared
later and turbulent intensity was slightly weaker in winter than in other
seasons. Averaged diurnal variation of turbulent intensity at different
heights during the year of 2009 is shown in Fig. 4. Three-dimensional
components of turbulent intensity decreased with increase in height. From the
height of 40 to 120 m, the u, v and w components of turbulent
intensity reduced by 27, 32 and 21 %, respectively. From 120 to 220 m,
the u, v and w components reduced by 12, 13 and 15 %, respectively.
The descending trend is more obvious from 40 to 120 m than from 120
to 220 m. This indicates that there were full vertical and horizontal
turbulence exchanges below 120 m of the tower, but relatively weaker
exchanges over 120 m.
Averaged NPBL height in each season (before dawn 01:00–07:00; at
night: 19:00–24:00 GMT + 8:00).
Vertical distribution of PM2.5 mass
concentration
The diurnal variation of PM2.5 mass concentrations during the period
from 1 July to 30 September 2009 is shown in Fig. 5. The vertical variation
patterns of PM2.5 concentrations were quite different during the daytime
and night resulting from a combination of diurnal variations of emissions and
PBL. After sunrise, the PBL height starts to rapidly increase, pollutants
near the ground gradually diffuse upward and the PM2.5 concentration
near the surface gradually decreases. At noon, the mixing layer is fully
developed with the averaged PBL height being about 1000–1200 m. Among these
four platforms (2, 40, 120 and 220 m), PM2.5 concentration at 220 m is
the highest during noon and afternoon. In contrast, after
18:00 GMT + 8:00, the PBL height starts to rapidly decrease. The NPBL
height generally ranges between 100 and 150 m (Fig. 3). At the first and
second platform (2, 40 m), the measured PM are normally inside of the NPBL.
By contrast, the measurement platform at 220 m is generally outside the
NPBL. Level 3 (120 m) is considered as being at the transition zone between
the inside and outside of the NPBL. Due to the dynamical stability of the
NPBL, the vertical mixing of pollutants between inside and outside of the
NPBL is very weak. The surface emitted PM are normally trapped inside the
NPBL, leading to the difference in the amount of aerosols below and above the
NPBL. Among these four platforms, PM2.5 concentration at 220 m during
the night is the lowest. This indicates that the observation value of 220 m
at night is less affected by local sources of emission and is largely
attributed to regional-scale pollution.
Averaged diurnal variation of three-dimensional components of
turbulent intensity at different heights (longitudinal turbulent intensity
Iu, lateral turbulent intensity Iv, vertical turbulent intensity
Iw).
Vertical distributions of PM10 concentration,
composition and source apportionmentVertical characteristics of PM10 concentration
As mentioned in Sect. 2.2, PM10 filter samples were collected at the
heights of 10, 40, 120 and 220 m. The daily concentrations at each sampling
height were 139 ± 45, 121 ± 43, 110 ± 39 and
79 ± 37 µg m-3, respectively. These concentrations
exhibited a general decreasing trend with the increase of height.
The height-to-height correlation coefficients of the variation of PM10
concentration were calculated and listed in Table 1. All the pairwise
correlation coefficients among 10, 40 and 120 m were higher than 0.9.
However, the correlation coefficients between 220 m and other heights were
obviously low. These results suggest that the influences of local emissions
and local meteorological diffusion conditions on PM10 concentrations are
weaker at 220 m than at lower levels.
Vertical characteristics of PM10 chemical
composition
Coefficient of divergence (CD) analysis (Wongphatarakul et al., 1998;
Krudysz et al., 2009) was used in this study to assess vertical variability
of chemical elements in PM10 filter samples collected at four heights. The
CD values provide information on the degree of uniformity between sampling
sites and is defined as
CDjk=1p∑i=1pxij-xikxij+xik2,
where xij is the average concentration of the ith element at
jth height. The j and k represent the two sampling heights, and p is the number
of elements. When the species concentrations at two sampling sites were
similar to each other, the CD values would approach 0. On the other hand, as
the two species concentrations diverge the CD value will approach 1 (Hwang
et al., 2008).
Vertical diurnal variation of PM2.5 mass concentrations during
the period from 1 July to 30 September 2009.
The pair-wise CD values for four heights are shown in Table 2. The pair-wise
CD values among 10, 40 and 120 m are lower than 0.2, illustrating that the
element profiles of these three heights were similar to each other, while
the CD values between 220 m and the other three levels were obviously high.
This may have resulted from the fact that chemical elements in the PM10 filter
samples collected at 220 m were mainly originated from regional-scale
sources.
The concentration of chemical composition in ambient PM10 filter samples
collected at four heights are shown in Table 3. Al, Si, Ca, OC, EC, Cl-,
NO3- and SO42- have higher concentration levels than other
species. Al can be used as a source marker of coal combustion (Hopke, 1985);
Al and Si are the markers of soil dust (Liu et al., 2003), Ca is mainly
emitted from cement dust (Shi et al., 2009); EC can be identified as vehicle
exhaust emission (Li et al., 2004); Cl- is the marker for sea salt (Li
et al., 2004); and NO3- and SO42- are the markers of
secondary nitrate and sulfate (Liu et al., 2003). Higher concentrations were
found at lower sampling heights for almost all species (NO3- had the
highest value at 120 m). Unlike the species concentration, the vertical
distribution of species percentages (%) shows different patterns. Similar
fraction levels were observed at the four heights for Al and Si. For Ca and
EC, higher values were observed at lower sampling sites. The percentages of
OC at 220 m were obviously higher than those at 120 m. This might imply
that the influence of local sources on OC was weaker and the contributions
from secondary and regional sources were larger at 220 m. The OC / EC
ratios increased gradually from 10 to 220 m. This might be due to a
relatively higher percentage of SOC in OC at higher heights as a result of the
formation and regional transport of SOC (Strader et al., 1999). Similarly,
the higher sampling sites obtained higher fractions (%) for NO3-
and SO42- (the highest percentage of NO3- was observed at
120 m). These trends suggest that the impact of primary sources from the
ground decreased with the increase of height, while the impact of secondary
sources mainly influenced by regional sources becomes more prominent.
Height-to-height correlation coefficient of PM10
concentration.
The concentration of chemical composition in ambient PM10 at four
height sampling sites (µg m-3).
10 m 40 m 120 m 220 m MeanSD*MeanSDMeanSDMeanSDNa1.600.711.340.581.280.480.890.41Mg1.510.541.290.920.990.520.540.36Al6.32.55.92.14.91.74.01.7Si8.54.66.82.96.42.84.92.8PNDNDNDNDNDNDNDNDK1.410.721.020.441.110.680.700.35Ca7.12.85.12.04.62.22.51.6Ti0.230.120.190.120.240.200.290.53VNDNDNDNDNDNDNDNDCr0.040.030.040.030.050.040.040.04Mn0.090.050.060.030.060.030.040.02Fe2.511.222.081.211.921.091.090.80Ni0.010.020.010.010.020.030.030.05Co0.01NDNDNDNDND0.010.01Cu0.200.170.140.220.090.130.020.03Zn0.690.320.600.310.550.280.270.16BrNDNDNDNDNDNDNDNDBaNDNDNDNDNDNDNDNDPb0.060.060.060.060.050.050.030.03OC*13.56.210.84.69.63.87.33.1EC*7.02.25.32.04.41.83.01.6NH4+6.23.56.33.46.93.15.74.0Cl-6.45.35.64.15.03.01.71.2NO3-18.012.516.910.918.910.113.311.4SO42-27.420.626.117.525.316.419.716.2OC / EC1.912.792.032.262.202.102.401.90PM101404812044108418039
* SD: standard deviation; OC: organic carbon; EC: element carbon.
Vertical characteristics of PM10 sources
In order to understand the vertical characteristics of PM10 sources, the
chemical mass balance (CMB) model was applied for source apportionment at all
four sampling heights. The CMB model, a useful receptor model, has been
extensively used to estimate source categories and contributions to the
receptor based on the balance between sources and the receptor (Chow et al.,
2007; Watson et al., 2008). Further details of CMB can be found in the
relative literature (Watson et al., 1984, 2002; USEPA, 2004). The data set of
chemical composition in the PM10 samples during the measurement period
and the source profiles reported in our previous works (Bi et al., 2007) were
used in the CMB modelling.
Six source categories (coal combustion, crustal dust, cement dust, vehicle
exhaust, secondary sulfate and secondary nitrate) and their source
contributions (µg m-3) and percentage contributions (%)
estimated by the CMB model are listed in Table 4. The estimated source
contributions (µg m-3) of all the sources showed a downward trend
with the increase of height, whereas the percentage contributions (%) of
secondary sources (secondary sulfate and nitrate) presented a generally
increasing trend with the increase in height. This might be due to the fact
that for the secondary sources the particulate sizes are relatively smaller
and the residence time of fine particle is longer. Generally, secondary
sources can obtain a stronger influence from regional contributions (Gu et
al., 2011). That is to say, PMs at higher heights obtain more regional
contributions. To some extent, this could reflect characteristics
at the regional scale.
Vertical variation of periodicity for the time series of
PM2.5 concentrations
The periodic characteristics of particulate concentration and meteorological
variables can reflect different scales of atmospheric processes. In this
paper, the vertical variation period of PM2.5 mass concentrations are
analysed.
A time series of atmospheric pollutant concentration can be decomposed into
baseline and short-term components. Using the filtering method, short-term
fluctuations associated with the influence of local-scale pollution and
dispersion conditions can be extracted from the original measurements. After
the removal of local-scale effects, the time series of pollutant
concentrations can be reconstructed to reflect the regional-scale influence.
Source contributions and percentage contributions at four different
heights.
The wavelet transform can be used to analyse time series that contain
nonstationary signals at many different frequencies. In this paper, we chose
the Morlet wavelet which is extensively used in studies of climate change
and turbulence power spectrum analysis (Torrence and Compo, 1998). The
normalization mother wavelet is
ψ0η=π-1/4eiω0ηe-η2/2,
where η is the nondimensional time parameter and ω0 is the
nondimensional frequency. The wavelet filter time series over a set of
scales can be calculated by
xn=δjδt1/2Cδψ00∑j=0JRWnsjsj1/2,
where δj is the spacing between the discrete scales, and δt
is the sampling interval. Sj is a set of scales related to the
frequency ω. Cδ and ψ00 are both
constants:
ω=ω0+2+ω024πs.
The reconstruction then gives
Cδ=δjδt1/2ψ00∑j=0JRWδsjsj1/2.
According to the conservation of total energy under the wavelet transform and
the equivalent of Parseval's theorem for wavelet analysis, the variance of
the time series is
σ2=δjδtCδN∑n=0N-1∑j=0JWnsj2sj.
Both Eqs. (7) and (8) should be used to check wavelet routines for accuracy
and to ensure that sufficiently small values of s0 and δj have
been chosen. The values of the above parameters are given in Table 5.
As discussed above, the wavelet transform is essentially a bandpass filter.
By summing over a subset of the scales in Eq. (5), a wavelet-filtered time
series can be constructed as follows:
xn′=δjδt1/2Cδψ00∑j=j1j2RWnsjsj1/2.
This filter has a response function given by the sum of the wavelet
functions between scale j1 and j2.
Values of the parameters of the Morlet transform in this study.
Cδψ0s0δtδjω00.776π-1/42δt20.256.0Fluctuation spectrum analysis of PM2.5 concentration time series at different heights
The fluctuation spectrum distribution of hourly mass concentrations of
PM2.5 on the ground and at the height of 2, 40, 120 and 220 m have been
analysed in this paper. Missing data in the time series were computed by
interpolation. Because of low proportions and unconcentrated distributions in
the missing data, little human interference was applied to the spectral
composition of the original time series. For better comparison, normalization
(standard variance 1, mean 0) of the original time series was necessary prior
to power spectrum analysis.
Local (left panels) and global (right panels) wavelet power spectrum
of PM2.5 mass concentration at different heights in August 2009.
The local and global wavelet power spectrum contours for the time series of
PM2.5 concentrations at different heights in August are shown in Fig. 6.
Contours are expressed as log2Wns2 because of large magnitudes. The area inside the thick
black solid line passes the red noise standard spectral test with the 5 %
significance level. The area outside the blue dotted line was excluded from
analysis because of poor reliability from the cone of influence, where edge
effects become important. The global wavelet spectrum W2‾s, which reflects characteristics of the pollutant
concentration time series in the frequency domain, was obtained by
calculating the average of local wavelet spectra Wns2 over the entire sampling time domain. The solid line is
the global wave spectrum for the corresponding time series. The dashed line
is the 5 % significance level, the upper area of which passes the red
noise standard spectral test at the 5 % significance level.
The global wavelet power spectrum of PM2.5 mass concentration shows that
fluctuations of 6–10 days (related to weather process and regional-scale
pollution) are significant at each observation height, while fluctuations of
12–24 h (mainly concerned with the daily variation of atmospheric boundary
layer and local pollution emissions by human activities) are significant only
at ground level. For the fluctuations of PM2.5 mass concentration, wave
energy of 6–10-day period reduces with the increase of height. In terms of
the local power spectrum, a 12–24 h period can be observed in a few days on
the ground. But with the increase of height, the power of the 12–24 h period
became weaker, only 10–30 % of that on the ground.
Time series of PM2.5 hourly concentration before and after
filtering.
Determination of regional background concentration of particulate
matter
Regional PM background concentration can hardly be measured directly.
Original PM concentration time series measured on the ground reflect a
combination of influence from local pollution and regional-scale pollution.
This study is expected to establish an approach to characterize the regional pollution
contribution and to evaluate regional background PM concentration levels.
According to the above research concerning the vertical distribution
characteristics of particle size, chemical composition and pollution sources,
the atmospheric boundary layer structure, as well as the fluctuation power
spectrum analysis of particle mass concentration, the measurement height
(influenced relatively less by local pollution emission) was determined and
impacts from local-scale pollution on the short-term fluctuations
removed from the original PM concentration by wavelet transformation. The
nocturnal PM2.5 mass concentration time series with the 6–10-day
period at the observation height of 220 m was extracted to characterize the
regional background concentration, which is mainly associated with the regional-scale pollution within 102 km of the measurement tower.
Time series of PM2.5 hourly concentration before and after the filtering
is presented in Fig. 7. Due to short-term fluctuations of pollution emission
and local diffusion conditions, observation errors etc., the original
PM2.5 concentration time series exhibits a violent oscillation. Using
wavelet transformation, the nocturnal PM2.5 mass concentration time
series with the 6–10-day period at the height of 220 m was extracted from
the original time series. After the filtering, impacts from local-scale
pollution and diffusion conditions on the short-term fluctuations were
considered to be removed. Thus regional-scale pollution and synoptic-scale
weather conditions were better represented in the remaining part compared
with the original PM concentration time series.
The swings in the PM2.5 concentration data (shown in Fig. 7) mainly
resulted from several meteorological processes during the measurement.
According to the meteorological data set of the observation station (WMO
Id. No. 54517), precipitation processes were recorded during the period of
22–24 July, with the amounts of rainfall ranging from 3.2 to 94.6 mm,
followed by a rapid decrease in PM2.5 concentration on 25 July due to
consequent cleaning of the air. Then, beginning on 26 July, mist paired with
calm winds caused a build-up of PM2.5 concentration until 29 July.
Similar meteorological processes were reported during the period of
22–25 August, 4–9 and 20–25 September, which resulted in the cycle of
cleaning and build-up of air pollutants.
According to the method proposed in this paper, in Tianjin, the averaged
regional background PM2.5 concentrations in July, August and
September 2009 were 40 ± 20, 64 ± 17 and
53 ± 11 µg m-3, respectively.
Summary and conclusions
It is crucial for studying regional-scale PM pollution and for the
development of efficient joint control policy to improve the understanding of the
regional background concentration of PM. The purpose of this study is to
characterize the regional pollution contribution and to evaluate regional
background PM concentration levels. However, regional background
concentration can hardly be measured directly. Original PM concentration time
series measured on the ground reflect a combination of influence from local
pollution and regional-scale pollution. A method to estimate regional
background PM concentration is proposed in this paper, based on the vertical
variation periodic characteristics of particle mass concentration, the
atmospheric boundary layer structure, as well as the vertical distribution of
chemical composition and pollution source apportionment.
Based on a 255 m meteorological tower, the vertical thermodynamic and
dynamic characteristics of the atmospheric boundary layer in Tianjin was
observed. The atmospheric layer at 100–150 m is considered as a transition
layer, the variation patterns of temperature and wind speed with height were
different compared with the upper and lower layers. A weak vertical gradient in
the temperature profile was observed over 100 m. Similarly, a small vertical
gradient in wind speed was found over 150 m. The turbulent intensity
decreased with increase in height and the descending trend is more obvious
from 40 to 120 m than from 120 to 220 m, which indicates that there
were full vertical and horizontal turbulence exchanges below 120 m of the
tower, but relatively weaker exchanges over 120 m. Seasonal averaged
nocturnal planetary boundary layer height ranged from 114 to 142 m. The
observation height of 220 m is just outside the NPBL, which indicates that
the observation value of PM concentration at 220 m at night is less affected
by local primary sources near the ground and is largely the result of
regional-scale pollution.
The vertical distribution of chemical composition in PM10 filter samples
also suggests that the impact of primary sources near the ground decreased
with height, whereas the impact of secondary sources mainly influenced by
regional sources became more prominent. The vertical distribution of
percentage was different for various species. Similar percentage levels were
observed at the four different heights for Al and Si. For the Ca and EC
fractions, higher values were observed at lower sampling sites. The
percentages of NO3-, SO42- and OC, and the OC / EC ratios
were obviously higher at higher sites. Source apportionment for ambient
PM10 showed that the percentage contributions of secondary sources
obviously increased with height, while the contribution of cement dust
decreased with height. PM at higher height obtained more regional
contributions, and to some extent it could reflect the characteristics of
the regional scale.
The periodic characteristics of PM2.5 mass concentration can reflect
different scales of atmospheric processes. In terms of the global wavelet power
spectrum of PM2.5 mass concentration, fluctuations of 6–10 days,
related to weather processes and regional-scale pollution, were significant
at each observation height, while fluctuations with 12–24 h period, mainly
concerned with the daily variation of atmospheric boundary layer and local
pollution emissions by human activities in the surface layer, were
significant only at ground level. In terms of the local power spectrum, a
12–24 h period can be observed in a few days on the ground. But with the
increase of height, the power of the 12–24 h period became weaker – only
10–30 % of that on the ground.
According to the above research, the nocturnal PM2.5 mass concentration
time series with the 6–10-day period at the measurement height of 220 m can
be regarded as regional background concentration, mainly associated with the
regional-scale pollution within 102 km of the measurement tower. Using
wavelet transformation and filtering, the nocturnal PM2.5 mass
concentration time series with the 6–10-day period at the height of 220 m
was extracted from the original time series. After removing the impacts from
local-scale pollution and diffusion conditions on the short-term
fluctuations, regional-scale pollution and synoptic-scale weather conditions
were better represented in the remaining part compared with the original PM
concentration time series. According to the method proposed in this paper, in
Tianjin, the averaged regional background PM2.5 concentrations in July,
August and September 2009 were 40 ± 20, 64 ± 17 and
53 ± 11 µg m-3, respectively.
We have put forward a new method to estimate the regional background
concentration of PM. Background PM concentrations are not constant but
vary with space and time. In future research, more analysis on the
characteristics of the urban boundary layer, vertical distribution of PM
composition and source apportionment in different seasons and meteorological
conditions will be done, and background concentration ranges of PM2.5
for given time periods and meteorological conditions will be obtained.
The Supplement related to this article is available online at doi:10.5194/acp-15-11165-2015-supplement.
Acknowledgements
This work was funded by the Tianjin Science and Technology Projects
(14JCYBJC22200), the Science and Technology Support Program (13ZCZDSF02100),
and the National Natural Science Foundation of China (NSFC) under Grant
No. 41205089 and No. 21207069. We also thank LetPub (www.letpub.com)
for linguistic assistance during the preparation of the
manuscript. Edited by: M. Shao
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