risk-based air pollutants management at regional levels
TRANSCRIPT
Risk-based air pollutants management at regional levels
Jianhua Xu a,b, Xuesong Wang c,*, Shiqiu Zhang a
aDepartment of Environmental Management, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR ChinabCenter for Crisis Management Research, School of Public Policy and Management, Tsinghua University, Beijing 100084, PR ChinacDepartment of Environmental Sciences, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, PR China
e n v i r o n m e n t a l s c i e n c e & p o l i c y 2 5 ( 2 0 1 3 ) 1 6 7 – 1 7 5
a r t i c l e i n f o
Published on line 8 November 2012
Keywords:
Risk-based approach
Regional air pollution
SO2
Beijing–Tianjin–Hebei region
a b s t r a c t
The ultimate objective of abating air pollution is to protect the health and welfare of the
public, while in the past, health and welfare are seldom used as the direct policy target.
Often, emissions or concentrations are used as the direct target. In this paper, we put heavy
weight on the justification of using a risk-based approach to address air pollution problems,
and use a case study to demonstrate its technical feasibility in a Chinese setting. In the case,
we study the health risks associated with SO2 emissions from the different sectors in Beijing
and its surrounding areas (the Beijing–Tianjin–Hebei region), to inform control responsibili-
ty assignment and control option design. The emission inventory was classified by sectors
according to Chinese Standard Industrial Classification of All Economic Activities. The
Community Multi-scale Air Quality (CMAQ) modeling system is used to simulate the fate
and transport of SO2 in the study region. Intake fraction, which is defined as the incremental
intake per unit of pollutant released from a source or a category of sources, is borrowed to
indicate the marginal risk posed by SO2 from the major sectors. The results show that the
intake fraction avoided per unit of SO2 emissions abated from the four major sectors (power
sector, smelting and pressing of ferrous metals, manufacture of non-metallic mineral
products, and chemical industry) varies greatly, which implies that using a risk-based
approach has the potential to help improve the efficiency in resource utilization for assign-
ing pollutant control responsibilities and prioritizing pollutant control options.
# 2012 Elsevier Ltd. All rights reserved.
Available online at www.sciencedirect.com
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1. Introduction
The ultimate objective of abating pollutant emissions is to
protect the health and welfare of the public. Methods for
estimating damages to health and welfare from air pollution
are well established (e.g., USEPA, 1999; Bickel and Friedrich,
2005; Holland et al., 2005, 2011; Ho and Nielsen, 2007; Muller
and Mendelsohn, 2007), while in practice, in assigning control
responsibility and designing control options for conventional
air pollutants, health and welfare were seldom used as the
direct target, largely due to the huge amount of information
needed to estimate damages to health and welfare and the
* Corresponding author. Tel.: +86 10 62758784.E-mail addresses: [email protected] (J. Xu), [email protected]
1462-9011/$ – see front matter # 2012 Elsevier Ltd. All rights reservedhttp://dx.doi.org/10.1016/j.envsci.2012.09.014
inherent uncertainties embedded in such an estimation
(Tietenberg, 1995). Often, emissions or concentrations are
used as the direct target; handy examples include the
pollution levy system and SO2 total emissions control system
in China and the SO2 emission trading program in the United
States. It should be kept in mind that policies as such were
developed decades ago. As computing power increases, air
pollutants’ fate and transport modeling capabilities advance,
and risk assessment evolves, it becomes more feasible than
ever to use health risks posed by air pollutants as the direct
policy target to improve the efficiency of society’s resource
utilization. Actually, risk-based approaches have been widely
used to assess the impact of energy utilization and evaluate
.cn (X. Wang), [email protected] (S. Zhang).
.
e n v i r o n m e n t a l s c i e n c e & p o l i c y 2 5 ( 2 0 1 3 ) 1 6 7 – 1 7 5168
the cost and benefit of air pollution control policies in Europe
(Bickel and Friedrich, 2005; Holland et al., 2005, 2011). Concerns
on using health risks as the direct policy target includes
unequal treatment of people exposed in polluted air as people
in populous areas are given heavier weight (Tietenberg, 1995),
and the tediousness and uncertainties in estimating risks
incurred by individual sources (Wesson et al., 2010). In its 2004
report Air Quality management in the United States, the National
Research Council (NRC, 2004) recommended a risk-based
approach to enhance air quality management in the United
States. Wesson et al. (2010) demonstrated the technical
feasibility of applying a risk-based approach to address air
pollution problems in the United States. In this paper, we use
health risks as the direct policy target to inform air quality
management in the Beijing–Tianjin–Hebei region (Fig. 1) as an
exploration of its technical feasibility in a Chinese setting.
Using risks posed by air pollutants as the direct policy
target to address air pollution problems in China has been
discussed in academia for a while. Florig et al. (2002) advocates
the adoption of exposure-based controls of particulate
emissions to improve the economic efficiency of public health
protection, and proposes ways in which exposure-based
controls might be applied under the current regulatory regime
(e.g., setting atmospheric emission limits according to the size
of the population affected). Florig et al. (2002) also mentioned
that one of the barriers to exposure-based regulation is the
paucity of data and limited local analytic capacity in modeling
the fate and transport of air pollutants, especially in less
developed regions. In the book Clearing the Air: the Health and
Economic Damages of Air Pollution in China edited by Ho and
Nielsen (2007), an analysis integrating economic growth,
energy use, air pollution and health damages was depicted.
It details the estimation of national health damages posed by
total suspended particulate (TSP) and SO2 emissions from
different sectors (e.g., power plants, chemicals industry, and
metals smelting and pressing). Facing the challenge of sparse
pollution data and limited health studies in China, this
research used the derived relationship between exposure risk
and emission source characteristics in sampled cities to
estimate the exposure risk in other parts of the country,
Fig. 1 – The Beijing–Tianjin–Hebei r
avoiding the need to conduct air fate and transport modeling
work. This work has great implications on attributing national
health damage by sectors and setting national mitigation
priorities regarding TSP and SO2. Zhou et al. (2010) estimated
the health risks posed by PM2.5 and O3 from four sectors (power
plants, industrial sources, mobile sources, and domestic life)
in the Yangzte River Delta and found that the health risks
avoided per tonne of PM2.5, NOx, VOC or SO2 emissions
reduction in these sectors are quite different in this delta,
which is informative for setting PM2.5 and O3 control strategies
in the Delta.
Our study focuses on estimating health risks posed by SO2
emissions from the different sectors at a regional level using a
case study, which is expected to inform control responsibility
assignment and control options prioritization for primary
pollutants. In the following, we describe the case, depict the
method, show the results as we go through the procedures in
the method section, and then draw conclusions, followed by
implications.
2. Case description – the Beijing–Tianjin–Hebei region
The Beijing–Tianjin–Hebei region is chosen to conduct the
case study, because firstly this region is experiencing severe
air pollution, secondly detailed pollutant emission data in this
region is readily available, and thirdly policy makers in this
region is soliciting suggestions on developing regional air
quality management mechanisms.
The Beijing–Tianjin–Hebei region, consisting of Hebei
province and the municipalities of Beijing and Tianjin
(Fig. 1), is located in northeast China, and covers an area of
212,800 km2 (about 2.2% of the total land area in China). There
are 13 cities at a prefecture-level or above (the 11 cities in Hebei
plus Beijing and Tianjin) in this region with 6 of them having a
population over 1 million. The 13 cities are inter-linked by
vibrant commercial activities and convenient transportation
(Chen and Lu, 2008). This region has a population of about 100
million, accounting for 7.5% of the national total, and
egion and its location in China.
Fig. 2 – SO2 emissions and annual average SO2 concentrations in Beijing, Tianjin and Hebei province.
Data sources: China Statistical Yearbook (2001–2010); Beijing Environmental Statement (1998–2010); Tianjin Environmental
Statement (1998–2010); Hebei Environmental Statement (1998–2010).
e n v i r o n m e n t a l s c i e n c e & p o l i c y 2 5 ( 2 0 1 3 ) 1 6 7 – 1 7 5 169
contributes about 10% of the gross domestic products (GDP) in
2009 (National Bureau of Statistics, 2010a). Within the region,
the tertiary industry in Beijing, Tianjin and Hebei contributes
75.5%, 45.3%, and 35.2% of the local GDP, respectively, and the
secondary industry in Tianjin and Hebei contribute about
53.0% and 52.0% of the local GDP, respectively in 2009 (National
Bureau of Statistics, 2010a). The stock of motor vehicles in two
mega-cities (Beijing and Tianjin) increased exponentially in
the past ten years, and reached 5 million in Beijing1 and 1.8
million in Tianjin in 2011. In 2009, the coal consumption in this
region amounted up to 333 million tonnes, accounting for
10.3% of the total national consumption (National Bureau of
Statistics, 2010b).
The Beijing–Tianjin–Hebei region suffers from severe air
pollution. Wind-blown dust, coal combustion, vehicular
emissions, and industry activities all contribute to the
unhealthy air in this region. Comparing daily Air Pollution
Index (API) in 86 major cities in 2010,2 we found that Beijing,
Tianjin and Shijiazhuang (the capital of Hebei province)
ranked 5, 19 and 24, respectively, in the medium value of
daily API, and ranked 4, 8 and 22, respectively, in the number of
days on which API exceeds 100 (100 is a value below which the
air quality is regarded as acceptable and posing little harm to
the health of the general public).
Anthropogenic SO2 are largely emitted from the burning of
fossil fuels and the extracting of metals from ores, in which
sulfur and its compounds exist as ‘‘impurities’’. In 2003, China
consumed 31% of the world’s coal. Burning coal contributes
more than 90% of the national SO2 emissions (Zhang, 2007). A
total annual amount of 22 Tg SO2 was discharged on average
in the most recent ten years, which is about 1/3 of the global
level (Smith et al., 2001; Stern, 2005). The SO2 emissions and
annual average concentrations during the past ten years in
the study region are shown in Fig. 2. The downward trend in
1 Beijing Traffic Management Bureau, http://ww.bjjtgl.gov.cn/publish/portal0/ (accessed 18.01.12).
2 The daily Air Pollution Index (API) of 86 major cities are avail-able on the website of the Ministry of Environmental Protection.http://datacenter.mep.gov.cn/. In mid-2010, more cities are addedto the list. Now, the daily API of 120 cities are available online.
both emission and concentration can be seen in the past five
years. Particularly, since 2009, the annual average SO2
concentrations in Beijing, Tianjin and Hebei all fall below
60 mg/m3, which is the grade II value in the Chinese National
Ambient Air Quality Standard (CNAAQS). However, the
monitoring data from June 2009 to December 2010 in 25 sites
covering the study region, provided by researchers from the
Institute of Atmospheric Physics, Chinese Academy of
Sciences, shows that the daily SO2 concentrations in this
region during the heating season (November–March) quite
often violate grade II daily SO2 CNNAQS (150 mg/m3). SO2
pollution is still severe albeit the improvement.
3. Method and results
The method for estimating health risks from air pollutants
emissions, which is an instance of the driving forces,
pressures, states, impacts and responses (DPSIR) framework
(Kristensen, 2004), has been well-documented and widely
applied (USEPA, 1999; Bickel and Friedrich, 2005; Ho and
Nielsen, 2007). Normally, it involves all or part of the following
procedures: (1) create emission inventories, (2) model the fate
and transport of pollutants, (3) estimate the distribution of the
population and their exposure, and (4) evaluate the health
risks posed by the pollutants. In the following sub-sections, we
detail how the procedures described above are followed to
estimate the health risks associated with SO2 emissions from
the different sectors in the study region.
3.1. SO2 emissions in the study region
We use 2006 SO2 emission inventory, which was constructed
in previous research on air quality assurance for the Beijing
Olympics by our colleagues and their collaborators. Based on
this inventory, the total emission of SO2 in the Beijing–
Tianjin–Hebei region reached 2.07 Tg in 2006 which is 5%
higher than the emissions provided in China Statistical
Yearbook 2007 (National Bureau of Statistics, 2007). The
emission inventory is classified by sectors according to the
Chinese Standard Industrial Classification of All Economic
Table 1 – The top 10 sectors in SO2 emissions in the study region.
Sector Emission (tonnes) Percentage Cumulative percentage
Production and distribution of electric power and heat power 1,544,443.4 74.91 74.91
Smelting and pressing of ferrous metals 141,016.1 6.84 81.75
Manufacture of non-metallic mineral products 109,643.8 5.32 87.07
Manufacture of raw chemical materials and chemical products 73,053.8 3.54 90.62
Processing of petroleum, coking, processing of nuclear fuel 40,259.2 1.95 92.57
Manufacture of paper and paper products 19,375.5 0.94 93.51
Mining and washing of coal 18,471.0 0.90 94.41
Manufacture of foods 9924.9 0.48 94.89
Manufacture of textile 8736.6 0.42 95.31
Manufacture of metal products 6725.9 0.33 95.64
Table 2 – SO2 emission reduction scenarios designed forsimulation.
Scenario Scenario description
Scenario 1 Baseline scenario, no actions are taken
Scenario 2 Solely reduce 30% of the emissions from
point sources in the sector of ‘‘production
and distribution of electric power and heat
power’’
Scenario 3 Solely reduce 30% of the emissions from
point sources in the sector of ‘‘smelting and
pressing of ferrous metals’’
Scenario 4 Solely reduce 30% of the emissions from
point sources in the sector of ‘‘manufacture
of non-metallic mineral products’’
Scenario 5 Solely reduce 30% of the emissions from
point sources in the sector of ‘‘manufacture
of raw chemical materials and chemical
products’’
Scenario 6 Solely reduce 30% of the emissions from
point sources in the sectors other than the
above four major sectors
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Activities, and a total of 95 sectors are identified. In the
original inventory, the emission sources in Hebei were well-
classified, while the emission sources in Beijing and Tianjin
were not. The Beijing and Tianjin sources were then classified
manually according to the products and services they provide
which are obtained from the website according to their
names. Table 1 shows the top 10 sectors in SO2 emissions in
the study region. The top 4 emissions sectors account for
more than 90% of the total regional emissions, which are the
major sectors that we are interested in.
3.2. Modeling
We would like to investigate how the emission reductions in
the major sectors affect the concentrations of SO2 in sub-areas
of the region. The major sectors includes the top 4 emission
sectors which are ‘‘production and distribution of electric
power and heat power’’, ‘‘smelting and pressing of ferrous
metals’’, ‘‘manufacture of non-metallic mineral products’’,
and ‘‘manufacture of raw chemical materials and chemical
products’’. The sub-areas are delineated following the juris-
dictional scopes (see Fig. 1). Hebei is divided into 11 areas
according to the jurisdiction of the 10 prefectures and the
capital city; Beijing is divided into 1 urban area (covering 6
districts) and 10 peripheral districts/counties; and Tianjin is
divided into 1 urban area (covering 6 districts) and 12
peripheral districts/counties. Six emission reduction scenari-
os are designed (Table 2).
The Community Multi-scale Air Quality (CMAQ) modeling
system is used to simulate the fate and transport of SO2 in the
study region. CMAQ is an open-source model capable of
modeling tropospheric ozone, fine particles, acid deposition,
and visibility degradation, developed by USEPA.3 The fifth-
generation Pennsylvania State University/National Centre for
Atmospheric Research Mesoscale Model (Grell et al., 1994) is
used to simulate the meteorological fields to drive CMAQ. The
gridded and speciated hourly emission inputs for CMAQ are
prepared using the Sparse Matrix Operator Kernel Emissions
model (Houyoux et al., 2000).
As SO2 is mainly emitted from sources burning fossil fuels
and all the year round daily SO2 concentrations during the
heating season frequently exceed the health-based standard,
we focus on the heating season in doing our simulation. We
choose December 2006 to conduct the simulation. In November,
heat supply gradually starts, while in January and February,
3 http://www.epa.gov/asmdnerl/CMAQ/index.html.
people celebrate the Chinese Lunar New Year. The model is
validated using observed data at Shangdianzi where a World
Meteorological Organization Global Atmosphere Watch moni-
toring station is located. Shangdianzi, about 100 km to the
northeast of Beijing urban area, is located in Hebei province and
Fig. 3 – Comparison of simulated and measured hourly SO2
concentrations.
Fig. 4 – The SO2 concentrations under Scenario 1 (baseline
scenario).
e n v i r o n m e n t a l s c i e n c e & p o l i c y 2 5 ( 2 0 1 3 ) 1 6 7 – 1 7 5 171
regarded as a clean background area in northern China. Hourly
SO2 concentrations are measured at this station and used to
validate the modeling results. It can be seen that the modeling
results and the measured data agree well (Fig. 3).
We would like to understand how the reduction of SO2
emissions in major sectors would impact SO2 concentrations
in the different areas within the Beijing–Tianjin–Hebei region.
Ideally, we would like to conduct a brutal force simulation on
the impact of the reduction of different amounts of SO2 in each
sector. However, it is computationally costly to conduct such
an undertaking. Instead, we designed six scenarios on SO2
emission reductions and then use linear extrapolation to
derive the impact of other SO2 emissions reduction scenarios
based on the results of these six scenarios. The simulated
results are shown in Figs. 4 and 5.
3.3. Estimating health impact
The adverse health and ecological effects associated with SO2
pollution are well-documented (e.g., Ware et al., 1981;
Moolgavkar et al., 1995; Likens et al., 1996; Sunyer et al.,
2003; Larssen et al., 2006). Exposure to SO2 may cause
respiratory diseases and lead to premature death. High
concentrations of SO2 and particulate matter with SO42�, a
product of SO2’s atmospheric reaction, being one of its major
components are found to contribute significantly to the
Fig. 5 – The degree of decrease in SO2 concentrations under Scenarios 2–6.
Table 3 – Intake fraction avoided per tonne of SO2
emissions reduced in the major SO2 emission sectorsand the ‘‘other’’ sector (Scenario 6).
Sectors iFa
Production and distribution of electric power
and heat power
4.73 � 10�7
Smelting and pressing of ferrous metals 6.46 � 10�7
Manufacture of non-metallic mineral products 7.62 � 10�7
Manufacture of raw chemical materials and
chemical products
4.36 � 10�7
Others 9.34 � 10�7
4 Guojia huanjing baohu shi er wu guihua [the National 12th Five-Year Plan for Environmental Protection], prepared by the Ministryof Environmental Protection, released by the State Council onDecember 15, 2011.
e n v i r o n m e n t a l s c i e n c e & p o l i c y 2 5 ( 2 0 1 3 ) 1 6 7 – 1 7 5172
elevated episodic respiratory morbidity and mortality in the
London fog in 1952 (Bell and Davis, 2001). A significant
association between the concentration of SO2 and the daily
mortality through the year was found in Beijing in 1989, and
the mortality risk was estimated to increase by 11% as the
concentration of SO2 doubles (Xu et al., 1994). Strong
association between SO2 exposure and respiratory ailments
has been found in such big cities as Shenyang and Chongqing
as well. SO2 plays a significant role in the formation of
regional haze which haunts almost all the city-clusters in
China, especially in winter. Besides the esthetic and health
impact, regional haze incurs enormous cost to the aviation
industry and the ground transportation sector. SO2 is also one
of the important precursors of acid rain, which can affect fish
populations, crop yields, and forests growth and survival, and
corrode buildings (Larssen et al., 1999, 2006). For most of the
ground-level air pollutants, people’s attention is more on
their health impact; while for SO2 pollution, the ecological
risk incurred by acid rain is of particular concern. In the
specific case of the Beijing–Tianjin–Hebei region, acid rain is
not severe because alkaline soil dust blown by wind helps to
neutralize the acidity of the emissions (Larssen et al., 1999).
Thus, we focus on the health risks associated with SO2
exposure.
In estimating health risks, we use the concept of intake
fraction to estimate the marginal benefit per tonne of SO2
emissions reduced (Eq. (1)) in the major sectors. Intake fraction
is defined as the incremental intake per unit of pollutant
release from a source or a category of sources (Bennett et al.,
2002). We slightly modify it to be the intake avoided per unit of
pollutant reduced for our purpose (Eq. (1)).
iFa ¼PN
k¼1 POPk � Ck � RER
(1)
where iFa denotes the fraction of intake avoided, k is the index
of sub-areas in the study region, N is the total number of sub-
areas in the region, POPk is the size of population in sub-area k,
Ck is the change in SO2 concentration in sub-area k, R is the
average breathing rate of an adult, and ER is the emission
reduced during a given time. In our research, the total number
of sub-areas is 35 (see the first paragraph in Section 3.2), the
sizes of the population in the 35 sub-areas are obtained from
provincial statistical yearbooks, the changes in SO2 concen-
tration in each of the 35 sub-areas are obtained by using
simulation, R is set to be 20 m3/days, and ER is the emission
reduction in December in each of the major sectors following
the six emission reduction scenarios set in the modeling part.
Scenario 1 is the baseline scenario, i.e., no emission reduction.
Here, we present the estimated intake avoided per tonne of
SO2 emissions in Scenarios 2–6, which represents the risks
avoided per tonne of SO2 emission reduction in the four major
sectors and the ‘‘other’’ sector (Scenario 6). The results are
shown in Table 3.
We use intake instead of health outcomes (e.g., respiratory
disease) to indicate the degree of health risks, as the major
purpose of our research is to estimate the marginal contribu-
tion to health risks by reducing one unit of SO2 emissions from
the different sectors and then to inform pollutant control
responsibility assignment and pollution control option priori-
tization. If we want to estimate the benefit of air pollution
control policies, then it is necessarily to estimate the number
of cases of the different health outcomes.
4. Conclusions and implications
In the previous SO2 control practices in China, the impacts per
tonne of SO2 from the different sectors are implicitly treated as
the same. As indicated in our case study, the risks reduced per
tonne of SO2 emission reduction in the different sectors are
quite different. The intake fraction avoided per tonne of SO2
emissions reduction in the sectors of ‘‘production and distribu-
tion of electric power and heat power’’, ‘‘smelting and pressing
of ferrous metals’’, ‘‘manufacture of non-metallic mineral
products’’, ‘‘manufacture of raw chemical materials and
chemical products’’ and ‘‘the aggregate of the other sectors’’
varies greatly. This means it will help improve the efficiency in
resource utilization by using a risk-based approach to assign
pollutant control responsibilities and prioritize pollutant
control options. The case study also demonstrates the
technically feasible to apply a risk-based approach to address
regional air pollution problems in a Chinese setting, which is of
great practical value for the following reasons.
Firstly, regional air pollution in China has become an issue of
great concern (Shao et al., 2006; Chan and Yao, 2008), while the
existing jurisdiction-based air quality management mecha-
nism is inadequate in addressing regional air pollution. There is
a strong political will in utilizing a regional mechanism to
manage air quality in China. As economy grows and urbaniza-
tion progresses, 28 city-clusters (city-cluster and region are used
interchangeably here) have emerged in China (Fang et al., 2005).
In these city-clusters, the economic activities are vibrant, the
populations are dense, and consequently the environmental
stresses are high. The air quality management programs in
China are designed and implemented on a jurisdictional basis,
while air pollutants (e.g., SO2, PM and O3) travel across
administrative borders (Peking University, 2007; DiGiovanni
and Fellin, 2005; Guo et al., 2009; Yan et al., 2005). Regional
concerted efforts are needed to address regional air pollution
problems. Being aware of this, the Chinese government in its
National 12th Five-Year Plan for Environmental Protection
(2010–2015)4 lists preventing and controlling air pollution on an
e n v i r o n m e n t a l s c i e n c e & p o l i c y 2 5 ( 2 0 1 3 ) 1 6 7 – 1 7 5 173
air-shed or regional basis as one of the key programs to be
carried out.
Secondly, the regional social-economic development dis-
parity legitimates a regional (instead of national) effort in air
quality management as well. In the vast land of China, the
eastern coastal provinces are relatively developed and the
central and western provinces are less developed. For
instance, in 2009, the per capita gross municipal product of
Shanghai (a coastal city) reaches 79,000 Chinese Yuan which is
the highest in China while the per capita gross provincial
product of Guizhou (a province in southwest China) is 10,300
Chinese Yuan which is the lowest in China (National Bureau of
Statistics, 2010a). This developmental disparity has been
taken into consideration in assigning pollution control
responsibilities among the provinces in China’s 11th Five-
Year Plan. One of the compulsory targets of the 11th Five-Year
Plan is to cut national SO2 emissions from the 2005 level by 10%
till the end of 2010. This target is then allotted to provinces
disproportionally considering the status of economic devel-
opment, the gravity of local environmental pollution and
historical provincial SO2 emissions, with a reduction degree
ranging from 0.0% (Tibet) to 25.9% (Shanghai) (SEPA and NDRC,
2007). Thus, practically, it makes more sense to design risk-
based air quality management schemes at regional levels than
at a national level, as new polices building upon the current
system are easily get implemented.
Thirdly, the situation of lacking the data to develop risk-
based air quality management strategies in China has been
greatly alleviated, at least in relatively developed regions.
Driven by the deterioration of the environment, the increase in
the general public’s environmental awareness and the growth
of the national wealth, a substantial amount of resources has
been allocated to address environmental problems in China
over the past ten years (MEP, 2000–2010). Many pollutant
emission and concentration data have been accumulated,
especially in mega-cities such as Beijing, Shanghai and
Guangzhou. Catalyzed by such mega-events as the 2008
Beijing Summer Olympics, Shanghai Expo 2010, and Guangz-
hou 2010 Asian Games, quite a few big projects have been
conducted to identify air pollutant emission sources, simulate
the fate and transport of air pollutants, and design pollution
control strategies for the host cities and their immediate
vicinities. Nationally, taking 2007 as the baseline year, the first
national pollution survey was conducted in China in 2008. The
aggregated 2007 pollution information was released in 2010
jointly by Ministry of Environmental Protection, National
Bureau of Statistics of China, and Ministry of Agriculture (MEP
et al., 2010), while the detailed pollution inventory is not
publicly available yet. It is expected that more and more
environmental data will be made publicly available in the near
future as a result of the demand for environmental informa-
tion transparency from the public. In this regard, it becomes
more feasible for governmental agencies and research
institutes to conduct risk-based air quality research to support
environmental decision making.
Fourthly, the health risks associated with one unit
pollutant emissions from the different sources could be quite
different considering the relationship between emissions and
health risks (Fann et al., 2009; Levy et al., 2009). The
meteorological factors, the health status of the individuals
exposed, the size and location of the emissions sources, the
height of the stacks, and the proximity of the population all
affect the strength of such a relationship. Facing the large
number of sources, it is computationally tedious and
administratively burdensome to estimate the health risks
posed by emissions from each of them. Often, the sources are
grouped by sector and then sector-level pollutant emissions
and health risks are estimated (e.g., Ho and Nielsen, 2007;
Zhou et al., 2010). Of course, this implies that sources within a
sector are treated equally regarding their impact per unit of
emissions. Comparing with a risk-based pollution control
strategy on individual sources, a sector-level emission control
strategy has less impact on the competitiveness of enterprises
within a sector and thus less impedance from enterprises.
Finally, different from most of the city-clusters in the
developed world, where the primary pollutants (e.g., SO2) are
substantially reduced and the secondary pollutants (e.g., PM2.5
and O3) are the major policy targets, the city-clusters in China
face both severe primary and secondary air pollution. It is
well-recognized that a mix of policies is needed to address the
complex air pollution problems in city-clusters in China. We
choose SO2 to demonstrate how a risk-based approach can be
used to improve the efficiency in resource utilization by
guiding the assignment of pollutant control responsibility and
the design of pollutant control strategies, and would like to
study PM2.5 and O3 with a similar approach in our ensuing
work.
It should be noted that we use the emission inventory of
2006 to demonstrate the feasibility of using a risk-based
approach to address regional primary air pollution problems.
Since then, five years has passed by and the 11th five year plan
has been fulfilled, during which, the SO2 emissions at a
national level and in our study region have been decreased by
14% and about 15%, respectively. Thus great care should be
taken in directly using the results of this research to guide
policy making. Nevertheless, the argument made and case
study conducted is expected to be a convincing advocacy to
the adoption of a risk-based approach to address regional
primary air pollution problems.
A risk-based approach helps improve the efficiency in
utilizing societal resources, but it is not a panacea. In
addressing regional air quality problems in China, a set of
policies are still needed, which include, but not limited to,
setting compulsory emission reduction targets by fully
considering the regional environmental capacity, avoiding
hot spots by continuing the restriction of constructing high-
polluting enterprises in designated areas, and paying particu-
lar attention to the area sources.
Acknowledgements
The authors would like to thank Desheng Huang, Yana Jin,
Xunzhou Ma, Quan Mu, Wei Wan, Dan Wu, Xuxuan Xie and
Xiuli Zhang for helping assigning the individual emission
sources to sectors, and Teng Nie for contributing to the CMAQ
simulation. The authors would also like to thank Yuesi Wang
from the Institute of Atmospheric Physics, Chinese Academy
of Sciences, for providing daily SO2 monitoring data covering
the study region from which we realized that the daily SO2
e n v i r o n m e n t a l s c i e n c e & p o l i c y 2 5 ( 2 0 1 3 ) 1 6 7 – 1 7 5174
concentrations during the heating season frequently exceeds
the health-based national air quality standards. The authors
are indebted to Weili Lin for sharing the monitoring data at
Shangdianzi which we used to validate our simulated results.
The research is supported by Beijing Municipal Commission of
Science and Technology under Grant No. D09040903670905,
Economy and Environment Program for Southeast Asia, and
the Public Welfare Projects for Environmental Protection
under Grant Nos. 200909003 and 201009002.
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Jianhua Xu is an environmental and energy associate professor atDepartment of Environmental Management, Peking University,and an adjunct research fellow at School of Public Policy andManagement, Tsinghua University. She got her PhD in engineeringand public policy from Carnegie Mellon University. Her researchinterest is in qualitative and quantitative decision analysis andrisk analysis related to environmental and energy policy.
Xuesong Wang is an associate professor at Department of Envi-ronmental Sciences, Peking University. He got his PhD degree inenvironmental science from Peking University. His research inter-ests include urban and regional air pollution modeling and as-sessment, atmospheric chemistry and transport.
Shiqiu Zhang is an environmental economic professor at Depart-ment of Environmental Management, Peking University. She isalso the Director of Institute of Environment and Economy, PKU.Her research interest is in the field of environmental economics,environmental management and policy.