# A Parameterized Method for Air-Quality Diagnosis and Its Applications

Authors: J. Z. Wang; S. L. Gong; X. Y. Zhang; Y. Q. Yang; Q. Hou; C. H. Zhou; Y. Q. Wang
Journal: Advances in Meteorology
 (2012)

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## Abstract

A parameterized method is developed to diagnose the air quality in Beijing and other cities with an index termed (parameters linking air-quality to meteorological elements PLAM) derived from a correlation between PM10 and relevant weather elements based on the data between 2000 and 2007. Key weather factors for diagnosing the air pollution intensity are identified and included in PLAM that include atmospheric condensation of water vapour, wet potential equivalent temperature, and wind velocity. It is found that the poor air quality days with elevated PM10 are usually associated with higher PLAM values, featuring higher temperature, humidity, lower wind velocity, and higher stability compared to the averaged values in the same period. Both 24 h and 72 h forecasts provided useful services for the day of the opening ceremony of the Beijing Olympic Games and subsequent sport events. A correlation coefficient of 0.82 was achieved between the forecasts and (air pollution index API) and 0.59 between the forecasts and observed PM10, all reaching the significant level of 0.001, for the summer period. A correction factor was also introduced to enable the PLAM to diagnose the observed PM10 concentrations all year round.


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## Body

## 1. Introduction

Meteorological elements, including water vapor content, surface wind speed, visibility, and the diurnal temperature range are important parameters to effect air pollutant concentrations [1]. Studies using data from seven air quality stations during the period of 2000 to 2008 in Taiwan area showed that PM10 and PM2.5 concentrations were significantly controlled by the weather elements, resulting in high concentrations in spring and winter and low concentrations in fall and summer [2]. Even in summer when relatively low pollutant concentrations were found, the weather elements still had a large impact on air quality as the high temperature and humidity tended to increase PM10 and PM2.5 concentrations, due to the formation of secondary pollution [3, 4]. Recently, substantial advances have been made in the studies of the impact of human activities on air quality caused by the increasing pollutant concentrations, especially ozone and particulate matters (PM) [5–9]. The concentration variations of different aerosols in various regions of China, as well as the impacts of aerosol concentration on sand/dust storms (SDS) and hazy weather in the Asia and North American were investigated [1, 10–14]. It is found that the variation of aerosol and gaseous concentrations is closely related to the changing weather elements [3, 8, 15–19]. The key weather elements influencing the air pollution include wind speed, wind direction, air pressure, temperature, humidity, precipitation, and atmosphere stability [20]. These factors control the dilution, transports, accumulation, and removal processes of air pollutants and hence dominate the air concentration of various pollutants under a given emission condition.The relationship between the evolution of the general circulation and foggy/hazy weather systems was also examined to link the wind/cloud fields with low-visibility weather, indicating that atmospheric aerosols such as mist, fog, fume, smoke, smog, and haze can be formed from a condensation or nucleation process [21–25]. Studies also show that in the case of heavy pollution due to the increase of NO2 and SO2, significant differences in condensability were observed before and after a hazy weather process. Before the haze onset, with the increase of SO2 and NO2 concentrations, condensability was increasing quickly. On the contrary, during a haze event, both SO2 and NO2 concentrations were decreased [19]. This was related to the consumption and washout of the nucleus in the haze formation. Atmospheric condensation is a key factor for diagnosing and forecasting the air pollution intensity in relation to weather conditions.The above studies indicated that meteorological elements have a significant control over the accumulation and dilution of air pollutants. One of the most important scientific questions is how to quantify the meteorological impacts and use them to diagnose and forecast the air quality under the assumption that the time scale of emission changes is much longer than the meteorology changes.Numerical prediction of air quality by 3D chemical transport models (CTM) has been implemented in a number of weather services around the globe with various degree successes [26–28] to forecast air quality. Due to the emission inventories used for the forecasts usually lagging behind the current date, this method has the limitation for an accurate forecast if the emission inventory is not updated to the current time for a region with ever changing industries such as in China. Through an analysis of observed PM and meteorological parameters, a parameterized method was developed and defined as the PLAM index. PLAM links the air pollution variations to the meteorological conditions. During the period of 2008 Beijing Olympic Games, PLAM was used to forecast the air quality in Beijing and achieved reasonable results.


## 2. Data and Methods

### 2.1. Data

For the sake of data comparison, analysis, and operational run, both PM10 and weather observations were collected simultaneously at the National Climate Observatory in Beijing (NCOB). PM10 is measured by a TEOM (Series 1400a Ambient Particulate Monitor). The instrument measures the PM10 mass concentrations every 5 minutes automatically. The mass transducer minimum detection limit is 0.01 μg. The precision for 10-minute and 1-hour averaged data is 5.0 μg m−3 and 1.5 μg m−3, respectively [29]. The daily mean PM10 data and weather observations were obtained after careful QA/QC processes. NCOB is one of the national climate observatories providing data for international exchange under Word Meteorological Organization (WMO) through World Weather Watch (WWW). The NCOB (N 39.8, E116.5, 32 m) station is in the suburban/rural area of Beijing. Compared with meteorological variables, PM measurements are substantially influenced by the local environment.


### 2.2. Correlation Analysis

Previous study has indicated that a positive correlation of PM10 observations with maximum temperature (tm) and relative humidity (rh) was found for the period from July to September in 2000–2007 at NCOB [30]. It was suggested that for a better signal analysis and forecasts, the peak PM10 values could be identified on a daily basis in the same month.In this study, for the forecasting purpose, the observed PM10 (y) with preceding 24 h weather elements xi(p, tm, w, rh, e, etc.) was used with daily average meteorological data for period from 1 July to 31 September 2000–2007 (total 736 days) managed by the National Climate Centre of China. The elements of p, tm, w, rh, evaporability, water vapour, dew point, wind direction, wind speed, cloudiness, rainfall, and so forth were included in the dataset. Figure 1 presents the correlations of PM10 values measured at NCOB with the maximum temperature, relative humidity, water vapor observed 24 hours before and with the evaporability at the same time. The evaporability is to describe the evaporation capacity of the atmosphere with a unit of mm. The correlations of PM10 with tm, rh, air pressure, water vapor amount and evaporability are given in Table 1. Positive correlations of PM10 observations were found with the maximum temperature, relative humidity, and water vapor with preceding 24 hours, and negative correlations were found with evaporability from July to September in 2000–2007 at NCOB. The correlation coefficients are 0.6039, 0.6246, 0.5458, and −0.2557, respectively, reaching a significant level over 0.001 [31].The correlation of PM10 with air pressure is also noted (Table 1).Table 1
Correlation analyses for PM10 with meteorological elements xi.
Element
b
0 (Slop)
b
1
Correlation coefficient
Significant level
Period
N
t
m
0.38
13.41
0.6039
>0.001
pre 24 h
736
rh
1.61
17.01
0.6246
>0.001
pre 24 h
736
w (water vapor)
0.41
36.13
0.5458
>0.001
pre 24 h
736
p
−0.02
0.01
0.6481
>0.001
Synchronization
736
Evaporability
−0.89
158.98
−0.2557
>0.001
Synchronization
736
Y
i
=
b
0
X
i
+
b
1.Correlation of PM10 with temperature (a), relative humidity (b), water vapor observed (c), and evaporability (d) preceding 24 h.
(a)
(b)
(c)
(d)Based on the analysis, a set of weather sensitive parameters signalling a forthcoming hazy event are identified, which can be observedseveral hours before the hazy weather occurs. Among these elements, high temperature, high humidity, moderate wind, and high stability will dominate the poor air-quality in the summer of Beijing. The effects of seasonal changes of meteorological elements on the PLAM will be discussed in next section. Therefore, the PLAM is established as a function of the following parameters:
(1)PLAM(F)∈f(p,t,w,rh,e,s,c′,…),
where p, t, w, rh, e, s, and c′ represent air pressure, air temperature, winds, relative humidity, evaporability, stability, and effect parameter associated with the contribution of air pollution β(c′), respectively.


### 2.3. Parameterization Method for Air-Quality Diagnosis

Some studies [32–34] have showed that the microprocesses in cloud physics can be described in a parameterization scheme with large-scale observations. It was also found that the stabilized summer weather with high air temperature, high relative humidity, moderate winds, and stability might create a microenvironment for a high PM10 concentration in August over Beijing [30], corresponding to a static dynamic forcing (baroclinicity) and thermal forcing (equivalent potential temperature (θe) gradient) in the atmospheric moist adiabatic processes [35]. It is shown that the lowest diffusion efficiencies occur more than 90% in moderate winds and stable conditions during the time for the summer period of July and August. Part of the temperature profile ranges between the adiabatic lapse rate (neutral stability) and the isothermal lapse rate [36]. The condensation is also a key factor for diagnosing pollution intensity under given weather conditions in the atmospheric moist adiabatic processes. Apparently, the final PLAM can be attributed to two separate factors: (1) initial meteorological conditions α(m)associated with the atmospheric condensation processes and (2) a dynamic effect parameter associated with the initial contribution of air pollution β(c′) such as
(2)PLAM=α(m)×β′(c).In the following sections, these two terms are derived and discussed in details.


#### 2.3.1. Initial Meteorological Conditionsα(m)

According to Gao et al. [37], the meteorological dynamical identification for haze and humid weather in summer Beijing indicated that, in general, the obvious variation of wet-equivalent potential temperature (θe) may be observed, which is associated with the releasing of latent heat by adiabatic condensation processes.Initial meteorological contribution can be expressed as the variation of wet-equivalent potential temperature (θe):
(3)α(m)=dθedt=θefcCpT,
where the condensation function fc is described by [19, 38]
(4)fc=fcd[(1+(L/Cp)(∂qs/∂T))p],
Where Cp is the heat capacity of air, L is the latent heat for condensation or evaporation of water vapor, and qs is the specific humidity. fcd is the dry condensation function as defined below:
(5)fcd=[(∂qs∂P)T+γp(∂qs∂T)p]γp=RdCpTP,
where Rd is the gas constant. In order to analyze the statistical relationship between air quality and meteorological parameters for the special period during the 2008 Beijing Summer Olympic Games (July to August, 2008), the data for July to August 2007 were used in calculating the correlation of PM10 with fc and θe in (3). Figures 2(a) and 2(b) show the correlations of the condensation function fc (4) and wet-equivalent potential temperature θe with the observed PM10 for July to August, 2007. It is seen that these two meteorological parameters are correlated with the PM10 and can be used for forecasting short term variations of PM due to changes in meteorology.Correlation of PM10 with fc (a) and θe (b) for July–August 2007.
(a)
(b)Thefc and θe only account for the meteorological contributions to the PM. In reality, the PM concentrations are effected by emissions, meteorology, and atmospheric processes such as nucleation, condensation, and dry/wet depositions.


#### 2.3.2. Relative Dynamic Affect Parameter

In order to quantify the relative impact of weather conditions on air pollution and eliminate the effect of total aerosol concentration change, a ratio of the initial weather conditions to the observed PM concentrations is introduced as the relative dynamic affect parameterμ:
(6)μ=α(m)c′,
where μ denotes the meteorological contribution increment per unit PM concentrations. Initial contribution of air pollution (c′) implicitly contains the information of all the processes on PM before a forecast, including the emission contributions.There exists a sharp seasonal variation in meteorological parameters. For example, the average temperature in Beijing can range from 28°C in summer to −8°C in winter, and relative humidity also varies a lot from summer to winter. The degree of the variation forα(m) with seasons is larger than that for PM10 observations. In order to derive a parameter applicable for a wider range of conditions, an adaptive function β′, is introduced
(7)β′=(1-μ)i-1μ,
which completes the definition of (2) for the PLAM.Forβ′ (μ,i), there exist several cases, depending on the size of the relative dynamic parameter value in this spectrum:(1)
μ≥1, i=1: favourable meteorological conditions for pollutants either to be diffused or to be maintained;(2)
0.5≤μ≤1, i=2: more adjustable weather conditions for pollutants to be accumulated;(3)
μ<0.5, i=3: most favourable weather conditions for pollutants to be accumulated.Figure3 shows the correlations of α(m) and PLAM with PM10 observations for a year. The slope of the correlation and the magnitude of α(m) reflects the impact degree of meteorology on PM concentrations. In dry and cold season of Beijing and northern China area (NDJF), the observed concentration of PM10 can reach as high as 400~450 μg m−3 and the initial meteorological influence parameter α(m) varies 0~20. Figure 3(a) also indicates that the air pollution situation in the winter time of Beijing is very heavy. However, the influences and contributions of meteorological conditions on the high PM10 were not high (shown in blue dot). In hot and humid season of Beijing and northern China area (JAS), the concentration of PM10 observations is less than 200 μg m−3. However, due to the hot and humid stabilizing weather conditions, α(m) is larger than that in the winter time, ranging from 40 to 70, which is the highest among all seasons (shown in green dot). In the transition season, that is, spring, (MAM), the concentration of PM10 observations can also reach as high as 400~600 μg m−3 with a medium contribution and influence of initial meteorological α(m) (in red dot). The correlation coefficient (r) of PLAM for all season with PM10 observations reaches 0.46 (R2=0.2116, Figure 3(b)), indicating that PLAM is a good indicator for air quality diagnosis.Correlations ofα(m) and PLAM with PM10 observations for a year.
(a)
(b)Because the levels of air pollutants are usually controlled by a combination of meteorological variables within the stable air masses, the impacts of individual meteorological variables on air pollution may not be significant and/or may be cancelled each other, as they do not account for the interrelation between variables. PLAM, as an integrated index, considered the interactions among the meteorological variables such as the individual wind direction, temperature and hence reflected the better performance than any individual variable.


## 2.1. Data

For the sake of data comparison, analysis, and operational run, both PM10 and weather observations were collected simultaneously at the National Climate Observatory in Beijing (NCOB). PM10 is measured by a TEOM (Series 1400a Ambient Particulate Monitor). The instrument measures the PM10 mass concentrations every 5 minutes automatically. The mass transducer minimum detection limit is 0.01 μg. The precision for 10-minute and 1-hour averaged data is 5.0 μg m−3 and 1.5 μg m−3, respectively [29]. The daily mean PM10 data and weather observations were obtained after careful QA/QC processes. NCOB is one of the national climate observatories providing data for international exchange under Word Meteorological Organization (WMO) through World Weather Watch (WWW). The NCOB (N 39.8, E116.5, 32 m) station is in the suburban/rural area of Beijing. Compared with meteorological variables, PM measurements are substantially influenced by the local environment.


## 2.2. Correlation Analysis

Previous study has indicated that a positive correlation of PM10 observations with maximum temperature (tm) and relative humidity (rh) was found for the period from July to September in 2000–2007 at NCOB [30]. It was suggested that for a better signal analysis and forecasts, the peak PM10 values could be identified on a daily basis in the same month.In this study, for the forecasting purpose, the observed PM10 (y) with preceding 24 h weather elements xi(p, tm, w, rh, e, etc.) was used with daily average meteorological data for period from 1 July to 31 September 2000–2007 (total 736 days) managed by the National Climate Centre of China. The elements of p, tm, w, rh, evaporability, water vapour, dew point, wind direction, wind speed, cloudiness, rainfall, and so forth were included in the dataset. Figure 1 presents the correlations of PM10 values measured at NCOB with the maximum temperature, relative humidity, water vapor observed 24 hours before and with the evaporability at the same time. The evaporability is to describe the evaporation capacity of the atmosphere with a unit of mm. The correlations of PM10 with tm, rh, air pressure, water vapor amount and evaporability are given in Table 1. Positive correlations of PM10 observations were found with the maximum temperature, relative humidity, and water vapor with preceding 24 hours, and negative correlations were found with evaporability from July to September in 2000–2007 at NCOB. The correlation coefficients are 0.6039, 0.6246, 0.5458, and −0.2557, respectively, reaching a significant level over 0.001 [31].The correlation of PM10 with air pressure is also noted (Table 1).Table 1
Correlation analyses for PM10 with meteorological elements xi.
Element
b
0 (Slop)
b
1
Correlation coefficient
Significant level
Period
N
t
m
0.38
13.41
0.6039
>0.001
pre 24 h
736
rh
1.61
17.01
0.6246
>0.001
pre 24 h
736
w (water vapor)
0.41
36.13
0.5458
>0.001
pre 24 h
736
p
−0.02
0.01
0.6481
>0.001
Synchronization
736
Evaporability
−0.89
158.98
−0.2557
>0.001
Synchronization
736
Y
i
=
b
0
X
i
+
b
1.Correlation of PM10 with temperature (a), relative humidity (b), water vapor observed (c), and evaporability (d) preceding 24 h.
(a)
(b)
(c)
(d)Based on the analysis, a set of weather sensitive parameters signalling a forthcoming hazy event are identified, which can be observedseveral hours before the hazy weather occurs. Among these elements, high temperature, high humidity, moderate wind, and high stability will dominate the poor air-quality in the summer of Beijing. The effects of seasonal changes of meteorological elements on the PLAM will be discussed in next section. Therefore, the PLAM is established as a function of the following parameters:
(1)PLAM(F)∈f(p,t,w,rh,e,s,c′,…),
where p, t, w, rh, e, s, and c′ represent air pressure, air temperature, winds, relative humidity, evaporability, stability, and effect parameter associated with the contribution of air pollution β(c′), respectively.


## 2.3. Parameterization Method for Air-Quality Diagnosis

Some studies [32–34] have showed that the microprocesses in cloud physics can be described in a parameterization scheme with large-scale observations. It was also found that the stabilized summer weather with high air temperature, high relative humidity, moderate winds, and stability might create a microenvironment for a high PM10 concentration in August over Beijing [30], corresponding to a static dynamic forcing (baroclinicity) and thermal forcing (equivalent potential temperature (θe) gradient) in the atmospheric moist adiabatic processes [35]. It is shown that the lowest diffusion efficiencies occur more than 90% in moderate winds and stable conditions during the time for the summer period of July and August. Part of the temperature profile ranges between the adiabatic lapse rate (neutral stability) and the isothermal lapse rate [36]. The condensation is also a key factor for diagnosing pollution intensity under given weather conditions in the atmospheric moist adiabatic processes. Apparently, the final PLAM can be attributed to two separate factors: (1) initial meteorological conditions α(m)associated with the atmospheric condensation processes and (2) a dynamic effect parameter associated with the initial contribution of air pollution β(c′) such as
(2)PLAM=α(m)×β′(c).In the following sections, these two terms are derived and discussed in details.


### 2.3.1. Initial Meteorological Conditionsα(m)

According to Gao et al. [37], the meteorological dynamical identification for haze and humid weather in summer Beijing indicated that, in general, the obvious variation of wet-equivalent potential temperature (θe) may be observed, which is associated with the releasing of latent heat by adiabatic condensation processes.Initial meteorological contribution can be expressed as the variation of wet-equivalent potential temperature (θe):
(3)α(m)=dθedt=θefcCpT,
where the condensation function fc is described by [19, 38]
(4)fc=fcd[(1+(L/Cp)(∂qs/∂T))p],
Where Cp is the heat capacity of air, L is the latent heat for condensation or evaporation of water vapor, and qs is the specific humidity. fcd is the dry condensation function as defined below:
(5)fcd=[(∂qs∂P)T+γp(∂qs∂T)p]γp=RdCpTP,
where Rd is the gas constant. In order to analyze the statistical relationship between air quality and meteorological parameters for the special period during the 2008 Beijing Summer Olympic Games (July to August, 2008), the data for July to August 2007 were used in calculating the correlation of PM10 with fc and θe in (3). Figures 2(a) and 2(b) show the correlations of the condensation function fc (4) and wet-equivalent potential temperature θe with the observed PM10 for July to August, 2007. It is seen that these two meteorological parameters are correlated with the PM10 and can be used for forecasting short term variations of PM due to changes in meteorology.Correlation of PM10 with fc (a) and θe (b) for July–August 2007.
(a)
(b)Thefc and θe only account for the meteorological contributions to the PM. In reality, the PM concentrations are effected by emissions, meteorology, and atmospheric processes such as nucleation, condensation, and dry/wet depositions.


### 2.3.2. Relative Dynamic Affect Parameter

In order to quantify the relative impact of weather conditions on air pollution and eliminate the effect of total aerosol concentration change, a ratio of the initial weather conditions to the observed PM concentrations is introduced as the relative dynamic affect parameterμ:
(6)μ=α(m)c′,
where μ denotes the meteorological contribution increment per unit PM concentrations. Initial contribution of air pollution (c′) implicitly contains the information of all the processes on PM before a forecast, including the emission contributions.There exists a sharp seasonal variation in meteorological parameters. For example, the average temperature in Beijing can range from 28°C in summer to −8°C in winter, and relative humidity also varies a lot from summer to winter. The degree of the variation forα(m) with seasons is larger than that for PM10 observations. In order to derive a parameter applicable for a wider range of conditions, an adaptive function β′, is introduced
(7)β′=(1-μ)i-1μ,
which completes the definition of (2) for the PLAM.Forβ′ (μ,i), there exist several cases, depending on the size of the relative dynamic parameter value in this spectrum:(1)
μ≥1, i=1: favourable meteorological conditions for pollutants either to be diffused or to be maintained;(2)
0.5≤μ≤1, i=2: more adjustable weather conditions for pollutants to be accumulated;(3)
μ<0.5, i=3: most favourable weather conditions for pollutants to be accumulated.Figure3 shows the correlations of α(m) and PLAM with PM10 observations for a year. The slope of the correlation and the magnitude of α(m) reflects the impact degree of meteorology on PM concentrations. In dry and cold season of Beijing and northern China area (NDJF), the observed concentration of PM10 can reach as high as 400~450 μg m−3 and the initial meteorological influence parameter α(m) varies 0~20. Figure 3(a) also indicates that the air pollution situation in the winter time of Beijing is very heavy. However, the influences and contributions of meteorological conditions on the high PM10 were not high (shown in blue dot). In hot and humid season of Beijing and northern China area (JAS), the concentration of PM10 observations is less than 200 μg m−3. However, due to the hot and humid stabilizing weather conditions, α(m) is larger than that in the winter time, ranging from 40 to 70, which is the highest among all seasons (shown in green dot). In the transition season, that is, spring, (MAM), the concentration of PM10 observations can also reach as high as 400~600 μg m−3 with a medium contribution and influence of initial meteorological α(m) (in red dot). The correlation coefficient (r) of PLAM for all season with PM10 observations reaches 0.46 (R2=0.2116, Figure 3(b)), indicating that PLAM is a good indicator for air quality diagnosis.Correlations ofα(m) and PLAM with PM10 observations for a year.
(a)
(b)Because the levels of air pollutants are usually controlled by a combination of meteorological variables within the stable air masses, the impacts of individual meteorological variables on air pollution may not be significant and/or may be cancelled each other, as they do not account for the interrelation between variables. PLAM, as an integrated index, considered the interactions among the meteorological variables such as the individual wind direction, temperature and hence reflected the better performance than any individual variable.


## 2.3.1. Initial Meteorological Conditionsα(m)

According to Gao et al. [37], the meteorological dynamical identification for haze and humid weather in summer Beijing indicated that, in general, the obvious variation of wet-equivalent potential temperature (θe) may be observed, which is associated with the releasing of latent heat by adiabatic condensation processes.Initial meteorological contribution can be expressed as the variation of wet-equivalent potential temperature (θe):
(3)α(m)=dθedt=θefcCpT,
where the condensation function fc is described by [19, 38]
(4)fc=fcd[(1+(L/Cp)(∂qs/∂T))p],
Where Cp is the heat capacity of air, L is the latent heat for condensation or evaporation of water vapor, and qs is the specific humidity. fcd is the dry condensation function as defined below:
(5)fcd=[(∂qs∂P)T+γp(∂qs∂T)p]γp=RdCpTP,
where Rd is the gas constant. In order to analyze the statistical relationship between air quality and meteorological parameters for the special period during the 2008 Beijing Summer Olympic Games (July to August, 2008), the data for July to August 2007 were used in calculating the correlation of PM10 with fc and θe in (3). Figures 2(a) and 2(b) show the correlations of the condensation function fc (4) and wet-equivalent potential temperature θe with the observed PM10 for July to August, 2007. It is seen that these two meteorological parameters are correlated with the PM10 and can be used for forecasting short term variations of PM due to changes in meteorology.Correlation of PM10 with fc (a) and θe (b) for July–August 2007.
(a)
(b)Thefc and θe only account for the meteorological contributions to the PM. In reality, the PM concentrations are effected by emissions, meteorology, and atmospheric processes such as nucleation, condensation, and dry/wet depositions.


## 2.3.2. Relative Dynamic Affect Parameter

In order to quantify the relative impact of weather conditions on air pollution and eliminate the effect of total aerosol concentration change, a ratio of the initial weather conditions to the observed PM concentrations is introduced as the relative dynamic affect parameterμ:
(6)μ=α(m)c′,
where μ denotes the meteorological contribution increment per unit PM concentrations. Initial contribution of air pollution (c′) implicitly contains the information of all the processes on PM before a forecast, including the emission contributions.There exists a sharp seasonal variation in meteorological parameters. For example, the average temperature in Beijing can range from 28°C in summer to −8°C in winter, and relative humidity also varies a lot from summer to winter. The degree of the variation forα(m) with seasons is larger than that for PM10 observations. In order to derive a parameter applicable for a wider range of conditions, an adaptive function β′, is introduced
(7)β′=(1-μ)i-1μ,
which completes the definition of (2) for the PLAM.Forβ′ (μ,i), there exist several cases, depending on the size of the relative dynamic parameter value in this spectrum:(1)
μ≥1, i=1: favourable meteorological conditions for pollutants either to be diffused or to be maintained;(2)
0.5≤μ≤1, i=2: more adjustable weather conditions for pollutants to be accumulated;(3)
μ<0.5, i=3: most favourable weather conditions for pollutants to be accumulated.Figure3 shows the correlations of α(m) and PLAM with PM10 observations for a year. The slope of the correlation and the magnitude of α(m) reflects the impact degree of meteorology on PM concentrations. In dry and cold season of Beijing and northern China area (NDJF), the observed concentration of PM10 can reach as high as 400~450 μg m−3 and the initial meteorological influence parameter α(m) varies 0~20. Figure 3(a) also indicates that the air pollution situation in the winter time of Beijing is very heavy. However, the influences and contributions of meteorological conditions on the high PM10 were not high (shown in blue dot). In hot and humid season of Beijing and northern China area (JAS), the concentration of PM10 observations is less than 200 μg m−3. However, due to the hot and humid stabilizing weather conditions, α(m) is larger than that in the winter time, ranging from 40 to 70, which is the highest among all seasons (shown in green dot). In the transition season, that is, spring, (MAM), the concentration of PM10 observations can also reach as high as 400~600 μg m−3 with a medium contribution and influence of initial meteorological α(m) (in red dot). The correlation coefficient (r) of PLAM for all season with PM10 observations reaches 0.46 (R2=0.2116, Figure 3(b)), indicating that PLAM is a good indicator for air quality diagnosis.Correlations ofα(m) and PLAM with PM10 observations for a year.
(a)
(b)Because the levels of air pollutants are usually controlled by a combination of meteorological variables within the stable air masses, the impacts of individual meteorological variables on air pollution may not be significant and/or may be cancelled each other, as they do not account for the interrelation between variables. PLAM, as an integrated index, considered the interactions among the meteorological variables such as the individual wind direction, temperature and hence reflected the better performance than any individual variable.


## 3. Annual Forecasts for 2008

To evaluate the performance of PLAM forecastability, an annual run was carried out to obtain the 24 hr forecasts of daily PLAM and compared with the observed PM10 at NCOB and the Air Pollution Index (API) defined by the Ministry of Environment of China taking into account of the air concentrations of NOx, NO2, SO2, TSP, and PM10. The API is calculated as follows:
(8)Ii=(Ci-Cij)(Cij+1-Cij)(Iij+1-Iij)+Iij,
where Ii is the API index for species i,Ci is the observed daily averaged concentrations for species i. Cij, Cij+1 and Iij, Iij+1 are the two standard daily averaged concentrations and API indices that are adjacent to the observed Ci and are determined from the lookup table (Table 2). The largest index for species i is regarded as the API index of the day. In China, SO2, TSP, and PM10 are usually the major air pollutants and correlated very well [23].Table 2
Standard air pollutant concentrations and indices.
API
500
400
300
200
100
50
Pollutant concentrations (mg/m3)
SO2
2.62
2.100
1.60
0.80
0.15
0.05
NO
x
0.94
0.750
0.565
0.15
0.10
0.05
NO2
0.94
0.750
0.565
0.28
0.12
0.08
TSP
1.00
0.875
0.625
0.50
0.30
0.12
PM10
0.60
0.500
0.420
0.35
0.15
0.05Figure4 shows the daily PLAM distribution in 24 h forecasts from 1 January to 31 December 2008. A good agreement was found between the forecasted and observed, including the daily variations, peaks and lows. There were a number of heavy pollution days between March and May as well as in September and December with PM10 greater than 200 μg m−3, among which there existed several extreme high pollution episodes around March 18, May 20–22, and May 27-28 with PM10 > 200–637, 280–604, and 200–450 μg m−3, respectively. These high episodes were all captured by the PLAM index. The features of comparison between the PLAM index forecast and PM10 observations at NCOB from 1 June to 31 August 2008 during the 2008 Olympic Games will be discussed in Section 4. The correlations between forecasted PLAM index and observed PM10 and API are shown in Figure 5. A correlation coefficient was achieved for 0.85 between the forecasts and API and 0.43 between the forecasts and observed PM10 for whole of the year 2008. All reached the significant level of 0.001. During the summer season from mid-June to late September, that is, 2008 Beijing Olympic Games period, the air quality was relatively high with low PLAM indices forecasted. Detailed forecasts will be given Figure 6 in Section 4.Figure 4
Daily PLAM distribution in 24 h forecasts, PM10 observed at NCOB from 1 January to 31 December 2008.Correlations of 24 h PLAM forecast results with API of Beijing (a) and actual PM10 in Beijing (b) for the period from 1 January to 31 December 2008.
(a)
(b)Figure 6
Daily PLAM distribution and PM10 observed in NCOB in Beijing.Comparing Figures5(a) and 5(b), API is calculated from the average PM10 measurements at different sites in the Beijing metropolitan area, indicating that the PLAM has the better capability to capture the regional pollution rather than a single site.


## 4. Applications of PLAM

### 4.1. Olympic Applications from June to September in 2008

Figure6 shows the time series of the PLAM index and PM10 observations at NCOB from 1 June to 31 August 2008. During this period of time, the averaged PLAM index was small (<50) with a declining rate of 35%, which signaled an improving meteorology for the period. However, a much larger declining rate of 83% for PM10 was observed. Under normal conditions, the variations of PLAM and PM10 should be rather consistent. The significant discrepancy of the variation trend should be caused by some nonmeteorological factors. It is well known that very active emission control measures were taken during the 2008 Olympic Games [3]. It is likely that the effective emission controls have contributed to the large declining rate for the PM10 in Beijing. Excluding the meteorological influence, that is, PLAM, the emission control may have contributed to a 43% reduction in PM10 from June 1 to August 31, 2008.To evaluate the performance of the PLAM method, the correlative analysis has been made on the 24 h PLAM forecasts and observations (Figure7). A correlation coefficient was achieved for 0.8209 between the forecasts and API and 0.5924 between the forecasts and observed PM10. All reached the significant level of 0.001.Correlations of 24 h PLAM forecast results with actual PM10 in Beijing Nanjiao (a) and API of Beijing (b) during the period of Beijing Olympic Game.
(a)
(b)The good agreement of the forecasted PLAM index with the API and PM10 indicates the ability of PLAM as a criterion for diagnosing air quality problem. The high correlation illustrates that the meteorology has a dominant control over the variation of air quality in Beijing.


### 4.2. PLAM Applications in Shanghai and Guangzhou

In order to check the applicability of the PLAM in various regions of China, two other cities were chosen, that is, Shanghai and Guangzhou, to evaluate the performance of the PLAM. Shanghai (31.1_N, 121.4_E, 8.2 m) is the largest city in Yangtze River Delta in East China with climatic characteristics different from North China and is located in the East Asian monsoon region with humid and rainy climate on the East coast of the Asian continent. Guangzhou (23.1,1_N 13.3_E, 7.3 m) located in the coast of South China has a subtropical climate features. Figure8(a) shows correlation analysis between the PLAM 24 h prediction and PM10 during January 1–December 31 in 2008 in Shanghai. Figure 8(b) shows correlation analysis between the PLAM 24 h prediction and of PM10 during January 1–May 31 in 2009 in Guangzhou. From Figure 8 it can be seen that the PLAM also applies to eastern and southern areas of China for different climatic characteristics regions in China.Correlation analysis between PLAM 24 h prediction and PM10 January 1–December 31, 2008 in Shanghai (a) and correlation analysis between PLAM 24 h prediction and of PM10 January 1–May 31, 2009 in Guangzhou (b).
(a)
(b)


## 4.1. Olympic Applications from June to September in 2008

Figure6 shows the time series of the PLAM index and PM10 observations at NCOB from 1 June to 31 August 2008. During this period of time, the averaged PLAM index was small (<50) with a declining rate of 35%, which signaled an improving meteorology for the period. However, a much larger declining rate of 83% for PM10 was observed. Under normal conditions, the variations of PLAM and PM10 should be rather consistent. The significant discrepancy of the variation trend should be caused by some nonmeteorological factors. It is well known that very active emission control measures were taken during the 2008 Olympic Games [3]. It is likely that the effective emission controls have contributed to the large declining rate for the PM10 in Beijing. Excluding the meteorological influence, that is, PLAM, the emission control may have contributed to a 43% reduction in PM10 from June 1 to August 31, 2008.To evaluate the performance of the PLAM method, the correlative analysis has been made on the 24 h PLAM forecasts and observations (Figure7). A correlation coefficient was achieved for 0.8209 between the forecasts and API and 0.5924 between the forecasts and observed PM10. All reached the significant level of 0.001.Correlations of 24 h PLAM forecast results with actual PM10 in Beijing Nanjiao (a) and API of Beijing (b) during the period of Beijing Olympic Game.
(a)
(b)The good agreement of the forecasted PLAM index with the API and PM10 indicates the ability of PLAM as a criterion for diagnosing air quality problem. The high correlation illustrates that the meteorology has a dominant control over the variation of air quality in Beijing.


## 4.2. PLAM Applications in Shanghai and Guangzhou

In order to check the applicability of the PLAM in various regions of China, two other cities were chosen, that is, Shanghai and Guangzhou, to evaluate the performance of the PLAM. Shanghai (31.1_N, 121.4_E, 8.2 m) is the largest city in Yangtze River Delta in East China with climatic characteristics different from North China and is located in the East Asian monsoon region with humid and rainy climate on the East coast of the Asian continent. Guangzhou (23.1,1_N 13.3_E, 7.3 m) located in the coast of South China has a subtropical climate features. Figure8(a) shows correlation analysis between the PLAM 24 h prediction and PM10 during January 1–December 31 in 2008 in Shanghai. Figure 8(b) shows correlation analysis between the PLAM 24 h prediction and of PM10 during January 1–May 31 in 2009 in Guangzhou. From Figure 8 it can be seen that the PLAM also applies to eastern and southern areas of China for different climatic characteristics regions in China.Correlation analysis between PLAM 24 h prediction and PM10 January 1–December 31, 2008 in Shanghai (a) and correlation analysis between PLAM 24 h prediction and of PM10 January 1–May 31, 2009 in Guangzhou (b).
(a)
(b)


## 5. Conclusions and Discussions

A parameterized method has been developed to predict the air quality in BISA and used successfully during the 2008 Beijing Olympic Games. The following was found.(1)
Water vapor condensation (fc), wet potential equivalent temperature (θe), air pressure, air temperature, relative humidity, and evaporability are found to be the key meteorological factors for diagnosing air pollution intensity.(2)
With the help of two independent data systems (PM10 values reported in API in Beijing and daily PM10 data collected by Beijing Nanjiao Observatory), the tests of PLAM performance showed that the forecasts have a cohesive correlation with them, reaching to a significant level.(3)
It is found that the correction factorβ′ enables the ability for PLAM to diagnose the observed PM10 concentrations all year round. The 24~72 h forecasts by PLAM provided valuable services for the day of opening ceremony and subsequent events during the whole Beijing Olympic Games. PLAM also was applied to eastern and southern areas of China for different climatic characteristics regions in China.



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*Source: 238589-2012-11-05.xml*