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Tracing Back the Smog: Source Analysis and Control Strategies for PM2.5 Pollution in Beijing-Tianjin-Hebei GUAN Dabo, LIU Zhu

China Environment Press


Tracing Back the Smog: Source Analysis and Control Strategies for PM2.5 Pollution in Beijing-Tianjin-Hebei GUAN Dabo, LIU Zhu

China Environment Press 路 Beijing, China


Editorial Board Editorial Board Members Guan Dabo/ University of Leeds Liu Zhu/ Kennedy School, Harvard University

Coordinator Liu Xiaozi

Huang Wei

Wang Qiuxia

Fang Lifeng

Lauri Myllyvirta/ Greenpeace

Photo Chen Qinggang

Simon Lim

Liu Feiyue

Translation Xiaozi Liu

Johannes Mauritzen

Lin Ke

Kuang Yin/ Greenpeace


Author Profiles

GUAN Dabo, PhD, is an associate professor at the University of Leeds, and a senior researcher and director of studies in Economics of St Edmund’s College, University of Cambridge. He has published around 50 articles in high quality journals covering the environment, economy and geography, and been frequently invited for comment in Nature and Science magazines. He has twice won first place in the Leontief Prize ( 2012, 2013 ) and Top Policy Paper at Environmental Science & Technology. He was the lead author for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change ( IPCC ).

LIU Zhu, PhD, is a Giorgio Ruffolo fellow for the Sustainability Science Program at Kennedy School, Harvard University. His research focuses on China’s sustainable development and a low-carbon pathway for coping with climate change. He has authored many academic publications in Nature Climate Change, Proceedings of the National Academy of Sciences of the United States of America ( PNAS ) and other journals. He was invited to write an article in Nature, titled ‘Energy policy: A lowcarbon road map for China.'


Executive Summary


II Executive Summary Severe air pollution and its associated health impacts

sources, little information is available on trends in

have become of major concern in China, and pollution

PM2.5 concentrations, and answers to the questions

control measures targeting heavily polluted areas

how much air pollution should be reduced and how

are top of the agenda at all levels of government.

to accomplish this reduction remain unclear. The

In September 2013, the State Council issued the

reality is that achieving the proposed PM2.5 target

Airborne Pollution Prevention and Control Action

remains a challenging task, especially considering

Plan ( 2013-17 ), pledging to improve air quality in the

the need for control measures that reflect the various

Beijing-Tianjin-Hebei area ( hereinafter referred to as

characteristics of the region. Therefore Greenpeace

the “Jingjinji” region ), the Yangtze River Delta and

has been co-operating with a team from the University

the Pearl River Delta. Soon afterwards, the Ministry of

of Leeds, UK, led by Dr. Dabo Guan, with the aim to

Environmental Protection ( MEP ) released a detailed

study PM2.5 sources and control strategies in Jingjinji,

implementation plan, aiming to reduce PM2.5 levels

since the end of 2012.

in Jingjinji by 25% and keep PM2.5 concentration in Beijing from exceeding a level of 60 µg/m3 by 2017.

This project report is the first of its kind to

However, even if PM2.5 is reduced by 25% every five

comprehensively analyze PM2.5 sources in the Jingjinji

years, the National Air Quality Standard Level II of

region and to assess to what extend the region

3

35µg/m will not be achieved until 2030.

should do to reach the air quality targets set by the MEP. The report aims to provide insight into PM2.5

As clear and concrete pollution reduction goals

pollution in the region, to fuel public debate, and more

at the central and local government levels are

importantly, to inform and influence decision-makers

being set, public concern for a deteriorating living

and stakeholders and provide rationale and support

environment continue to mount. Due to a lack of

for actions that reduce PM2.5 levels.

detailed analysis on PM2.5 composition and emission

Research Methodology Specifically, this study tries to answer the following questions for the Jingjinji region: How much are the annual PM2.5 emissions? What sources are contributing to PM2.5 in the region? Which sectors are emission-intensive?


III

Can we become more ambitious in our aims and meet national standards in a decade instead of by 2030? To what extend should the region be willing to make changes in order to achieve that? What key measures are required to reduce emissions?

We have tackled these questions by first compiling a sector and fuel-specific PM2.5 emission inventory ① including primary sources and precursor gases ( SO2, NOx, NH3 and VOCs ), then plugged this inventory into air quality models, in order to analyze PM2.5 chemical composition, and evaluate the trends of PM2.5 concentration in different baseline scenarios. On that basis, we have been able to suggest how much emissions to reduce so that the national standard can be met by 2022, and recommend a list of effective control measures that are in line with subregional profiles; e.g. structure of the economy, industrial composition, energy mix and emission sources.

The detailed methodology is presented in the following flow chart:

空气质量 GAINS Air模型 quality model GAINS

CMAQ Air quality model

PM2.5 Chemical composition Baseline PM2.5 concentration

2010 Emission Inventory

Control measure potential

PM2.5 source apportionment Primary PM2.5 & precursor gases

① Our emission inventory covers over 150 sectors and is based on data

from 2010. According to the China Energy Statistical Yearbook (2011 and 2012) and public information released by the MEP, our analysis on sector apportionment based on the 2010 emission inventory still holds for the situation in 2011 and 2012.


IV Main Findings

Efforts to reduce PM2.5 level in the Jingjinji region should consider simultaneous control of primary sources and precursor gases. Sulfate-Nitrate-Ammonium ( SNA ) aerosols that are transformed from SO2, NOx and NH3 and are the major constituents [1]

of PM2.5, making up 50%-70% of the total mass concentration. In Beijing, primary source emissions contribute to 40% of PM2.5 concentration, while precursors contribute to 60% of the mass concentration. In Tianjin, the percentages are 47% vs. 53%, and in Hebei 41% vs. 59%, respectively. Over-reliance on coal is the most important factor of high levels of PM2.5 in Jingjinji. If we break down the source contribution by fuel type ① , emissions from coal combustion

[2]

constitute 25% of primary PM2.5 emissions and account for 82% of SO2 and 47% of NOx emissions in that region. In comparison, combustion of oil products accounts for 4% of primary PM2.5, 31% of NOx and 18% of VOCs emissions. By sector ② contribution , coal-fired power generation is the largest single source for industrial PM2.5 pollution in Jingjinji with coal-fired power stations emitting 9% of

[3]

primary PM2.5, 69% of SO2 and 47% of NOx. Industrial production of steel, cement and brick are other major sources of PM2.5 emissions that account for 49% of primary PM2.5, 12% of SO2 and 17% of NOx in that region. Contribution by the transport sector to PM2.5 pollution appears more substantial in

[4]

Beijing than in Tianjin and Hebei. That sector accounts for 45% of total NOx emissions in Beijing. This makes the transport sector the second largest single source of industrial air pollution in the capital, following the power generation sector; The iron and steel, cement and brick-making industries take a greater toll on ambient

[5]

air quality of Tianjin and Hebei than of Beijing. In particular, industrial processes in Hebei province are the most important source of primary fine particles and account for 50% of direct emissions. To meet the PM2.5 standard level of 35 µg/m3 by 2022, it is necessary to reduce PM2.5

[6]

emissions in the region by 80%, SO2 by 60%, NOx by 75%, NH3 by 85% and VOCs by 90%.

① In this research, we considered five types of fuel for

source apportionment, i.e. coal, oil, gas, biogenic and non-combustion ( direct emissions or fugitive emissions ).

② The research divides sector-specific sources into six

subcategories, i.e. transport, industrial processes, energy ( coal-fired power plants ), commercial and households, agriculture and fuel production and others.


V A Pathway to Blue Skies

If the Jingjinji region is to meet the National Air Quality Standard Level II ( 35 Âľg/m3 ), it should focus on the following areas: 1) Limit the use of coal, especially by the utility industry, and ban all approval of any new coal-fired power plants. In its stead the region should tap into renewable energy sources; 2) Shutdown polluting and energy-intensive industries such as cement plants and iron and steel mills, and replace coal-fired boilers with gas-fired ones; 3) Upgrade existing small-scale boilers for domestic and commercial use by replacing coal-fired boilers with gas-fired ones, increase proportion of gas consumption in the domestic sector, and ban agricultural waste incineration; 4) Improve the quality of oil products and emission standards for vehicles. Based on our analysis of potential control measures, we hereby put forward these suggestions for reducing PM2.5 emissions in Beijing, Tianjin and Hebei by 2022:

Beijing Industrial processes are a major source of primary PM2.5, and the precursor gases ( SO2, NOx and VOCs ) are mainly emitted by the energy and transport sectors. Thus these three sectors should be the main targets. In terms of emissions by fuel type, combustion of coal and oil products and non-fuel emissions ( emissions during industrial processes ) are the major sources of gaseous precursors. Emission measures should concentrate on substantial reduction of coal use, improving quality of oil products and raising the emission standards for vehicles.


VI

Actions that should be put into place by 2022 include: [1] Shut down coal-fired

cement plants. Install fabric filters

[4] Escalate the adoption of end-

power plants within the capital

in cement kilns and ban new

of-pipe treatments in the electric

boundary, increase percentage

cement plants;

power sector. Install flue gas

of electricity from renewable

desulfurization and de-nitration

sources in total energy mix, and

[3] Apply the National VI

equipment for all fossil fuel power

boost distributed solar and wind

Emission Standard to light-duty

plants. Use low-NOx burners and

power. Renewable energy can be

gasoline cars and heavy-duty

install fabric filters;

sourced from surrounding areas;

diesel cars, and increase the percentage of buses and cabs

[5] Reduce VOCs emissions from

[2] Shut down all existing iron

fueled by clean energy to over

industrial processes.

and steel plants and most

40%;

Tianjin The energy sector ( coal-fired power plants ) is the biggest source of PM2.5 emissions. In terms of fuel contribution, emissions from coal burning and non-fuel escapement ( fugitive gas escaped from industrial processes ) play a main role. Efforts should be targeted at reducing fuel consumption and fugitive emissions during production of oil products. Actions that should be put into place by 2022 include: [1] Increase the use of wind

and oil-related products, impose

[4] Expedite the adoption of end-

power/distributed solar power

desulfurization measures, and

of-pipe treatment technologies in

and increase the use ratio,

reduce the emission of VOCs;

the electric power sector, realize

and significantly reduce the

simultaneous desulfurization

percentage of coal-fired power in

[3] Shut down the most polluting

and denitration of flue gas from

energy provision;

cement and steel plants, install

all fossil fuel power plants, use

fabric filters in existing cement

low-NOx burners, install fabric

[2] Tighten control of fugitive

kilns, and ban new cement and

filters, and shut down part of the

emissions during production of oil

steel plants;

existing coal-fired power plants.


VII Hebei The energy sector ( coal-fired power plants ) is the biggest PM2.5 emitters among all industrial sectors. The industrial processes sector is the main source of primary PM2.5 aerosol, however domestic and commercial sectors also contribute significantly. In terms of emissions by fuel combustion/use, mitigation efforts should focus on the two main fuel types, namely coal and non-fuel ( from industrial-process sector ). Actions that should be put into place by 2022 include:

[1] Invest in renewable power

equipment and fabric filters in

filters, and shut down part of the

generation to the maximum

iron and steel plants, and install

existing coal-fired power plants;

extent and use them to replace

fabric filters in cement kilns; [4] Upgrade existing small-

coal-fired power plants; [3] Expedite the adoption of end-

scale boilers for domestic and

[2] Accelerate the shutdown

of-pipe treatments in the electric

commercial use by substituting

of the most polluting iron and

power sector with the goal of

gas-fired boilers for coal-fired

steel plants, coking and cement

installing flue gas desulfurization

ones, use more gas instead of

plants and sectors plagued by

and de-nitration technology in

coal as household fuel, and ban

over-capacity problems. Also,

all fuel-fired power plants. Use

agricultural waste incineration.

install flue gas desulfurization

low-NOx burners, install fabric

A breakdown of total emissions by sub-region reveals Hebei emits the largest amount of pollutants. This is because the region is the most dominated by heavy industries, and also the impacts of small-scale boilers for residential and commercial purposes are greater in this region. If Hebei fails to reduce the emissions of PM2.5 and precursor gases in a timely and effective fashion, efforts to abate air pollution across the entire region will be undermined. While Beijing, Tianjin and Hebei are each tackling air pollution on their own, they should also build regional air pollution prevention and control mechanisms as soon as possible. Hebei province should be given priority in terms of allocation of resources. Ultimately, effective control of PM2.5 emissions has to integrate a full set of policy measures. These include reducing coal use in energy provision, strict enforcement of end-of-pipe treatment technology, monitoring of major emitting industries, upgrading small-scale boilers, and implementing regional air pollution monitoring and early warning and emergency response systems.


Table of Contents

Background

1

Methodology

5

2.1 Emission inventories

6

2.2 Baseline scenario design

8

2.3 CMAQ air quality model

9

2.4 Choices of emission

10

reduction measures

PM2.5 source apportionment

13

3.1 The chemical composition of PM2.5 14 in Jingjinji 3.2 Source analysis of PM2.5

PM2.5 concentrations under business-as -usual scenario

29

4.1 Estimation of current PM2.5

30

concentrations 4.2 Trends of PM2.5 concentrations

32

in 2010-2030

Strategies to achieve PM2.5 standards in Jingjinji

35

5.1 Reduction potentials of emission 36 policy measures

16

in the Jingjinji region 3.3 Source analysis of PM2.5 in Beijing 19 3.4 Source analysis of PM2.5 in Tianjin

22

3.5 Source analysis of PM2.5 in Hebei

25

5.2 A pathway to blue skies

39

Conclusions

45


Appendices Appendix 1: PM2.5 background information Appendix 2: The chemical composition of PM2.5

47

49

50

Appendix 3: A list of sector-specific emission reduction measures

51

Bibliography

55


1

Background


2 Background

Suspended Particulate Matter ( SPM ) is a major type

developing nation, China consumes a large amount

of air pollutant. Subtypes within SPM include Total

of energy and industrial products. It is the largest

Suspended Particles ( TSP; particles with a diameter

primary energy consumer in the world. In 2008-2011,

of 100 micrometers or less ), PM10 ( particles with a

China accounted for 80% of the total global growth in

diameter of 10 micrometers or less ) and PM2.5 ( particles

the use of coal. China's output of industrial products

with a diameter of 2.5 micrometers or less ). PM10

such as cement, steel and glass also accounted for

and PM2.5 that can be easily inhaled into lungs pose

more than half of the global total. Driven by a rapid

considerable health risks.

growth rate of energy consumption and industrial production, China has become the country with the

Because of their small diameter, toxicity, long

highest PM2.5 levels in the world ( van Donkelaar

suspension time in the air, and the long distances

et al., 2010 ), with the Jingjinji region’s level of fine

they can travel, PM2.5 can have a strong impact on

particles exceeding that of the Sahara. In the winter

human health. Relevant studies show that increases

of 2012, China experienced PM2.5 haze episodes

of PM2.5 level can significantly raise the risk of

with real-time PM2.5 levels in Beijing and some other

lung cancer and other diseases. For instance, for

regions exceeding unprecedented 1,000 µg/m3.

every 10 µg/m3 increase in PM2.5 level, lung cancer, cardiopulmonary disease mortality rates and low birth

Shocked by the environmental and health impacts of

weight are estimated to increase by 8%, 6% and

PM2.5, the Chinese public is keen to reduce emissions.

10% respectively ( Greenpeace, 2013a ). Another

The government is also committed to resolving

Greenpeace research report ( Pan, Li and Gao, 2012 )

the issue by taking bold action. In May 2010, nine

has revealed that current PM2.5 levels causes tens

ministries including the Minister of Environmental

of thousands of early deaths in Beijing, Shanghai,

Protection jointly released the Guidance of Promoting

Guangzhou and Xi’an annually. According to the

Joint Prevention and Control of Air Pollution and

World Bank’s research, between 2003 and 2006

Improving the Regional Air Quality. In February,

air pollution-induced death and disease cost China

Minister of Environmental Protection released a

1.16%-5.0% of GDP per year. According to a recent

new ambient air quality standard ( GB3095-2012 ),

World Health Organization ( WHO ) research report,

specifying 35 µg/m3 as the limit level of PM2.5. This

atmospheric particulate pollution has become the

was the first time that the state developed a national

fourth largest cause of death in East Asia.

ambient air quality standard for PM2.5. In October 2012, the State Council promulgated the 12th Five-

As the world's most populous country and the largest

Year Plan on Air Pollution Prevention and Control


3

in Key Regions. In December, the Jingjinji region,

of pollutants from primary and secondary sources.

Yangtze River Delta and Pearl River Delta as well as

Primary PM2.5 is emitted directly from combustion and

provincial capital cities established a PM2.5 monitoring

other sources. Secondary PM2.5 is formed from the

network and have been publishing real-time air quality

emission of non-particles ( i.e. precursor gases ) -

monitoring data ever since. In June 2013, the State

such as sulfur dioxide ( SO2 ), nitrogen oxides ( NOx ),

Council announced ten measures to curb air pollution

volatile organic compounds ( VOCs ) and ammonia

and planned to invest RMB1.7 trillion in the next five

( NH3 ) - that turn into PM2.5 in the atmosphere through

years. Three months later, the State Council issued

chemical reactions or condensation ( Appendix 1 ).

the Atmospheric Pollution Prevention and Control

Previous research mainly focused on primary PM2.5

Action Plan, identifying a mix of measures and aiming

emissions without giving due attention to the analysis

to monitor PM2.5 concentrations in real time in 40

of secondary PM2.5 transformed from precursor

cities. The MEP developed a detailed implementation

gases. This report has taken a holistic approach to

plan to curb air pollution in Beijing-Tianjin-Hebei

studying primary PM2.5 and precursor gases in the

and surrounding areas, endeavoring to reduce PM2.5

most polluted Jingjinji region. We first compiled a

levels in Jingjinji by 25% and keep PM2.5 in Beijing

PM2.5 emission inventory specified by sector and by

3

below 60 Âľg/m by 2017.

fuel type, then applied air quality models - CMAQ and GAINS - to simulate PM2.5 concentrations in baseline

However, whether those measures are sufficient to

scenarios. The report concludes by providing

win the battle against PM2.5 pollution is still uncertain;

emission reduction policy recommendations based on

meanwhile the public is urging the government to

the results on sector apportionment and on emission

provide a safer living environment earlier rather than

reduction potentials of each policy measure.

later. In a public opinion survey of the government’s goal of meeting MEP level II air quality standards

This report is the first of its kind to include PM2.5

by 2030, 70% of respondents in the major cities of

source analysis, concentration simulation and

the Jingjinji region were unsatisfied, and 92% of

emission reduction policy recommendations for the

respondents believed that the standard should be met

Jingjinji region. It aims to focus public attention on

by 2020 ( Greenpeace, 2013b ). In practice, immediate

PM2.5 air pollution in the region, and more importantly,

action combating PM2.5 pollution is challenging since

to influence decision-makers and stakeholders and

few sectorial analyses of PM2.5 emissions exist.

to provide rationale and support for reducing PM2.5 levels.

PM2.5 is not a single pollutant, but rather a compound


4


5

Methodology


6 Methodology Based on the emission inventory, we used multiple instruments including air quality models - Community Multiscale Air Quality Model (CMAQ) and Greenhouse Gas and Air Pollution Interactions and Synergies Model (GAINS) - to estimate current PM2.5 levels and evaluate concentration changes under different scenarios. Specific research methods are presented in the following flow chart:

空气质量 GAINS Air模型 quality model GAINS

CMAQ Air quality model

2010 Emission Inventory

PM2.5 Chemical composition Baseline PM2.5 concentration

Control measure potential

PM2.5 source apportionment Primary aerosols & precursor gases

2.1 Emission Inventories

Although our emissions inventory is based on

A complete list of air emission sources is needed to

contributions and overall trends still hold for 2011 and

get a precise understanding of PM2.5. Factoring in the

2012. According to China Energy Statistical Yearbook

structural characteristics of emission, the research

( 2011 and 2012 ) and information released by MEP,

has developed a detailed emissions inventory that

there are some, but not significant changes in the

covers both primary and secondary sources ( sources

growth of coal and oil demand, nor has there been

of fine particles that are formed in the atmosphere

a change in the rates of industrial coal consumption

from precursor gases SO2, NOx, NH3 and VOCs ) in

and installation rates of end-of-pipe facilities for coal-

2010. The inventory is specified by sector ( a total of

fired boilers ① .

2010 national statistics, our findings on source

150 sectors ) and by fuel type.

① Statistics show that in 2011, coal consumption and oil consumption increased by 10% and 8% respectively. The percentage

of coal consumed by coal-fired power plant increased to 36% as opposed to 35% in 2010. Meanwhile, industry has consumed a steady 22% of coal over the past two years. Installation rates of desulfurization and de-nitration facilities for coal-fired power plants have increased by 6% and 10% in 2010-2012.


7

Total PM2.5 emissions are calculated as follows:

PM2.5

EF

Where:

are indices for sector ( j ), fuel ( k ) and pollution control technique ( m );

are the total PM2.5 emissions summed over all j, k and m;

denotes activity level of a certain emission source, measured in ton of standard coal equivalent;

EF

is PM2.5 emission factorďźˆ kg/kJ ;

is the penetration rate of a control measure ( % );

is the pollutant removal efficiency of a control measure ( % ). There are five efficiency levels per sector.


8

2.2 Baseline Scenario Design

Pollution Interactions and Synergies Model

We developed a business-as-usual ( BAU ) scenario

GAINS model is provided by the International Institute

to forecast a trend of PM2.5 concentrations in

for Applied Systems Analysis ( IIASA ) and the model

Jingjinji during 2010-2030 that assumes no further

forecasts the emissions of various pollutants in five-

interventions are taken. The result of our BAU

year intervals. Specifically, the four built-in scenarios

scenario is an average of outputs from four baseline

in the GAINS model are:

( hereinafter referred to as “GAINS model” ). The

scenarios embedded in the Greenhouse Gas and Air

[1]

[2]

UNEP ( United Nations Environment Programme ). This scenario is based on the UN projection of China’s energy development.

IEA ( International Energy Agency ). This scenario is based on IEA projections of China’s energy development.

[3]

ECLIPSE ( Evaluating the Climate and Air Quality Impacts of Short-lived Pollutants ).

[4]

TOP ( Tokyo Ozone project ).

Please see the IIASA website ① for detailed assumptions for each of the four scenarios.

① http://www.iiasa.ac.at/


9

2.3 CMAQ Model

used in environmental decision-making. Since June 1998, when the first version was released, the US

The PM2.5 concentrations in this report are from simulation results using the CMAQ

Environmental Protection Agency ( EPA ) has been

model. CMAQ,

or Community Multi-scale Air Quality, is widely

Meteorology model ( WRF )

Meteorology -Chemistry Interface ( MCIP )

Emissions model ( SMOKE )

constantly developing and updating the CMAQ. We used version V4.7.1 that was released in June 2010.

Boundary condition ( BCON )

Chemical transport model ( CCTM )

Photolysis rates ( JPROC )

Initial condition ( ICON )

CMAQ Source: US EPA http://www.epa.gov/asmdnerl/ Research/RIA/cmaq.html

① http://cmascenter.org/cmaq/

② The troposphere is the lowest portion of Earth's atmosphere and the

densest layer of the atmosphere. It contains approximately 75% of the atmosphere's mass and nearly all of its water vapor and aerosols.


10 Many factors influence PM2.5 concentrations. The CMAQ model sees tropospheric

air as a whole, and

WRF ( Weather Research and Forecasting Model ) is used to simulate meteorological fields that are

takes into account meteorological, topographic and

necessary to the operation of CMAQ. To ensure

surface conditions, as well as physical and chemical

accuracy of the boundary meteorological fields,

actions that occur during emission, transmission and

each horizontal boundary in the WRF simulation

diffusion of pollutants. The CMAQ model has five

domain has three more grids than that in the CMAQ

major modules ( see the boxes in the above diagram ).

simulation domain. On the vertical plane, there are

CCTM is the key module used to simulate process of

23 σ layers and the atmospheric pressure of the

atmospheric pollutant transport, chemical conversion

top layer is 1hPa. MODIS ( or Moderate Resolution

and aerosol settlement.

Imaging Spectroradiometer ) land use data are amassed to calculate topographic and Earth’s surface

The simulation model is configured as follows. The

data.

vertical domain from the Earth’s surface to the top of the troposphere is divided into 14 unequal layers ( the atmospheric pressure of the top layer is 1hPa ).The closer to the surface of the Earth, the more concentrated the

2.4 Choices of Emission Reduction Measures

layers are. The model has triple-nested domains: the

To meet the National Air Quality Specification II

outer domain, with a horizontal grid spacing of 36 km,

of 35 µg/m3 by 2022, this research has developed

covers the entire East Asia region including China,

a comprehensive policy package to curb PM2.5

North Korea, South Korea and Japan; the 12 km grid-

emissions in the Jingjinji region. Compilation of the

spacing inner domain covers the relatively developed

package is based on the sensitivity test of each

eastern regions in China; the innermost domain

potential intervention policy that is embedded in the

covering Jingjinji and its surrounding areas has the

GAINS-city model ( Liu et al., 2013 ). The intervention

highest horizontal grid resolution of 4 km. The outputs

policy, specified by sector, covers measures

from the outer domains are used to provide boundary

necessary to reduce emissions of primary PM2.5 as

conditions for the inner ones by one-way nesting.

well as precursor gases that form secondary PM2.5

The outermost BCON fields are based on simulation

through a series of chemical reactions and physical

results of GEOS-Chem

.

processes. Broadly, the identified control policy can be summarized as follows:

① The troposphere is the lowest portion of Earth's

atmosphere and the densest layer of the atmosphere. It contains approximately 75% of the atmosphere's mass and nearly all of its water vapor and aerosols.

② http://geos-chem.org/


11 • Substitute clean energy sources for coal-based electricity: coal burning is a major source of the

• Adopt more effective end-of-pipe technology, e.g. installation of fabric filters to collect dust;

two PM2.5 precursor gases SO2 and NOx. The use of natural gas, wind power and PV to replace coal would substantially reduce PM2.5 emissions;

• Improve production technology, e.g. during cement production, replace shaft kiln production lines with dry cement manufacturing production lines;

• Promote energy cascade use. Recycle industrial waste energy, increase the percentage of thermal

• Limit and phase out energy-intensive industries.

plants based on combined heat and power,

Phase out and shut down pollution-intensive

and replace distributed household heating with

cement plants and iron and steel plants in the

centralized heating systems;

Jingjinji region;

• Improve the energy efficiency per unit of industrial

• Adopt stricter environmental protection standards.

production. Relevant research shows that the

For example, apply the national V and VI emission

energy efficiency of industrial production in China

standard for vehicles, and phase out all vehicles

is 30% lower than levels in developed Western

that are below the national II emission standards.

countries ( IEA, 2009 );

We have selected 79 top policy measures ( see Appendix 3 for a complete list ), based on their reduction potential performance. The reduction potential of each policy measure is evaluated according to the following equation:

Where: = types of pollutants, including primary PM2.5 and PM2.5 precursor gases like SO2, NOx, NH3 and VOCs.

= total emissions of pollutant i under a business-as-usual scenario.

= emission reduction of pollutants resulting from a specific control measure.


12


13

PM2.5 Source Apportionment


14 PM2.5 Source Apportionment In this section, we first show the chemical

emissions of precursor gases, i.e. SO2, NOx, NH3

composition of PM2.5 and the contributions of primary

and VOCs. CMAQ simulation results for PM2.5

and secondary sources to the total concentration

concentration reveal that in 2010 direct emissions

based on simulation results. We then present a study

of PM2.5 and precursor emissions are responsible

of primary PM2.5 emissions and PM2.5 precursor gases

for 40% and 60% of the total PM2.5 concentration in

( transformed into PM2.5 via chemical reactions ) to

Beijing, respectively. In Tianjin, the percentage is

trace the emission sources.

47% against 53%, and in Hebei, 41% against 59%. This clearly indicates that in order to reduce PM2.5

3.1 The Chemical Composition of PM2.5 in Jingjinji

concentrations, emission reductions of primary PM2.5 and PM2.5 precursor gases have to run in parallel.

PM2.5 comes from direct emission of fine particles, and also aerosols transformed from the direct

Figure 3-1: Contributions to PM2.5 mass concentration by NO3- and SO42- (as percentage of concentration)


15

Differences in industrial structure and the natural

Jingjinji, making up 50%-70% of the total mass

environment can explain variations in PM2.5

concentration ( Figure A1 in Appendix 2 ). The

composition. In the following, we will focus on the

concentrations and compositions of SNA aerosols

two major PM2.5 chemical species: carbonaceous

are determined primarily by their precursor emission

aerosols ( from direct emissions and secondary

such as SO2, NOx, and NH3. A breakdown of SNA

transformation ) and inorganic aerosols ( Sulfate-

composition shows that sulfate ( SO42- ) constitutes

Nitrate-Ammonium or SNA ).

30%-33% PM2.5 mass concentration ( Figure 3-1 ), while nitrate ( NO3- ) comprises 22%-24% and

Carbonaceous aerosol is comprised of Black

ammonium ( NH4+ ) 15%-16%.

Carbon â‘  ( BC ) and Organic Carbon ( OC ). Our model indicates that BC is only responsible for 5%-

SNA exists in forms of (NH4)2SO4 or NH4NO3, but

9% of PM2.5 concentration, as shown in Figure A1 in

the former is more stable than the latter. Under most

Appendix 2. BC resulting from incomplete combustion

conditions, NH3 reacts preferentially with SO2 to form

of fossil fuels or biogenic substances is often used

ammonium sulfate ( Damberg, 2007 ) that stays in

as an indicator to measure the scale of vehicle-

the atmosphere for a long time and travels to other

induced pollution ( Huang et al. 2006 ). The mass

regions, causing regional pollution ( Zhang, 2011 ).

concentration of BC in Beijing downtown area is the

NH4NO3 is unstable and its formation is constrained

highest in the Jingjinji region, which is in part due to

strongly by temperature ( Wang et al., 2006 );

higher car ownership numbers in Beijing. With respect

e.g. the low temperature in winter is favorable for

to OC, Hebei has the highest OC mass concentration

NH4NO3 formation. If control efforts focused solely

of 20%-22% ( see Figure A1 in Appendix 2 ). OC is

on SO2 reduction, the freed NH3 in the atmosphere

both a primary and secondary pollutant compound.

would form ( if conditions allow ) two units of

Primary Organic Aerosol ( POA ) is directly emitted

NH4NO3 instead of one unit of (NH4)2SO4. In the

from fuel combustion and other sources, and

short run, it might result in a temporary rise of PM2.5

Secondary Organic Aerosol ( SOA ) is transformed

concentration ( Wang et al., 2013 ). In other words,

from semi-volatile organic compounds.

emission reduction efforts should aim at simultaneous prevention and control of multiple pollutants.

Sulfate-Nitrate-Ammonium ( SNA ), inorganic aerosol, is the single most important PM2.5 component in

â‘  Black Carbon (BC) is also known as Carbon Black (CB) or Elementary

Carbon (EC).


16

3.2 Source Analysis of PM2.5 in the Jingjinji Region

3.2.1 Contributions by Region

Our emission inventory indicates that in 2010 over 10

million tons of primary PM2.5. A breakdown of

million tons of primary PM2.5 and its precursors were

emissions shows that Beijing, Tianjin and Hebei

emitted into the atmosphere in the Jingjinji region.

emitted 130 kilotons, 140 kilotons and 1.3 million tons

By region, Hebei is the largest emitter. By sector,

respectively.

In 2010, the Jingjinji region emitted a total of 1.6

the cement, iron, steel and brick-making industries as well as small-scale boilers for domestic and

Hebei province is not only the main emission source

commercial use are the major emitters of primary

for primary fine particles, but also their precursor

PM2.5. These sectors contribute to 80% of primary

gases. Total emissions of SO2 were about 3.5 million

PM2.5 in the region. As for precursor gases, coal-fired

tons with Beijing, Tianjin and Hebei responsible for

power plants are the major emitters, responsible for

9%, 15%, and 77% of them respectively. The total

69% and 47% of SO2 and NOx in the region. By fuel,

emissions of NOx were about 2.2 million tons with

coal burning is the major source of PM2.5, making up

Beijing, Tianjin and Hebei responsible for 12%, 13%

25% of primary PM2.5, and 82% of total emissions of

and 74%. Emissions of VOCs were about two million

SO2 and 47% of NOx.

tons in total, among which Beijing, Tianjin and Hebei

NOx

Figure 3-2: Total emissions of primary PM2.5 and precursors gases in Jingjinji, 2010


17

emitted 330 kilotons, 290 kilotons and 1.3 million

in the region, responsible for 48% of primary PM2.5

tons. Aggregate emissions of NH3 stood at one million

emissions, 12% of SO2 and 17% of NOx emissions.

tons approximately, with Beijing emitting 60 kilotons, Tianjin 50 kilotons, and Hebei 900 kilotons.

Heating for households and businesses is the third biggest source of pollution in Jingjinji, emitting 32%

3.2.2 Contributions by Sector

of primary PM2.5 emissions, 14% of SO2, 6% of NOx emissions and 25% emissions of VOCs.

In terms of total emissions, coal-fired power generation is the biggest source of pollution in

Although the transport sector emits relatively few

Jingjinji, responsible for 9% of primary PM2.5, 69% of

primary PM2.5 and specific pollutants like SO2, they

SO2 and 47% of NOx emissions.

discharge 29% of NOx and 14% of VOCs.

Industrial processes for making steel, iron and bricks are the second biggest source of pollution

NOx

VOCs

Figure 3-3: Total emissions of primary PM2.5 and precursor gases by sector in Jingjinji, 2010


18

3.2.3 Contributions by Fuel Type

for 25% of primary PM2.5, 82% of SO2 and 47% of NOx emissions. As for non-combustion emissions,

Four combustible fuel types have been considered

these account for 55% of primary PM2.5 in the Jingjinji

in our study: coal, oil, gas and biogenic substances.

region, 13% of SO2, 16% of NOx emissions, 98%

Emissions from non-combustion processes ( e.g.

of NH3 emissions and 53% of VOCs emissions. Oil

fugitive emissions, also particles from industrial

burning makes up 4%, 31% and 18% of primary

processes ) are classified as non-fuel or non-

PM2.5, NOx and VOCs emissions, respectively.

combustion, the fifth fuel type.

Burning biogenic materials results in 15% of primary PM2.5 emissions and 19% of VOCs emissions.

We have shown that coal burning is the leading source of pollution in the Jingjinji region, accounting

NOx

VOCs

Figure 3-4: Total emissions of primary PM2.5 and precursor gases by fuel type in Jingjinji, 2010


19

3.3 Source Analysis of PM2.5 in Beijing

3.3.1 Total Emissions

Our results show that coal burning is the largest

and PM2.5 precursors, including 300 kilotons of SO2,

source of PM2.5 pollution in Beijing, followed by oil

270 kilotons of NOx, 60 kilotons of NH3 and 330

combustion from the transport sector. The sector-

kilotons of VOCs.

In 2010, Beijing emitted 130 kilotons of primary PM2.5

wide results further indicate that energy ( thermal power generation ), transport, cement-making and steel-making are major polluting sectors in Beijing.

NOx

VOCs

Figure 3-5: Emissions of primary PM2.5 and precursor gases by sector in Beijing, 2010


20

NOx

Figure 3-6: Contributions of the industrial processes broken down by sub-sector in Beijing, 2010

3.3.2 Emissions by Sector

Although the energy sector only accounted for 11% of primary PM2.5, it was a key emitter of gaseous

In terms of the directly emitted PM2.5, energy sector

precursors, causing 56% of Beijing’s SO2 and 38%

( thermal power generation ) contributed the most

of NOx emissions ( Figure 3-5 ). Fuel consumption

in Beijing, followed by emissions from the transport

by transport sector is the largest source of Beijing’s

sector. The industrial processes sector ( e.g. cement-

NOx ( 45% ).

making, coking, and metal processing sectors and smelters ) contributed 41%, making it the top source

The emissions of NH3 in Beijing are largely caused

of primary PM2.5 in Beijing. The use of small-scale

by agricultural practices ( e.g. animal husbandry)

boilers ( with capacity below 50MW ) for households

and fertilizer production. In contrast, sources of

and commercial sector shares 32% direct emissions

volatile organic compounds ( VOCs ) emissions

of fine particles.

are diversified; e.g. fugitive emissions during oil


21

NOx

VOCs

Figure 3-7: Emissions of primary PM2.5 and precursor gases by fuel type in Beijing, 2010

transportation, leaking from small coke ovens for

77% of SO2 were formed from coal burning ( mainly

domestic and commercial use, and emissions during

for coal-fired power generation, but also used in

processes.

small-scale boilers for domestic and industries ); oil and coal burning were the key sources of NOx ( 53%

3.3.3 Emissions by Fuel Type

vs. 42% ).

Emissions from coal burning are the most important

In contrast to SO2 and NOx that are formed due to

source of PM2.5 pollution in Beijing. Of primary PM2.5,

fuel combustion, NH3 and VOCs are largely caused

non-combustion (particles formed during industrial

by fugitive emissions ( i.e. non-combustion ).

processes for making cement, glass, steel and bricks) and coal burning played a leading role ( 47% vs. 37% ).


22

3.4 Source Analysis of PM2.5 in Tianjin

Tianjin had a total of 29 coal-fired power plants, which consumed 25 million tons of coal or 52% of the total coal usage in Tianjin.

Based on our analysis on PM2.5 chemical composition and sector apportionment, the main source of PM2.5

3.4.1 Total Emissions

pollution in Tianjin is coal. In contrast, coal plays a much bigger role in Tianjin than in Beijing. The

In 2010, the discharge of primary fine particles was

energy sector ( coal-fired power plants ) consumes

140 kilotons, whereas emissions by the precursor

the greatest amount of coal and is thus the major

gases was 510 kilotons for SO2, 300 kilotons for NOx,

contributor of PM2.5 pollution in Tianjin. Statistics from

50 kilotons NH3 and 290 kilotons for VOCs.

the China Electric Yearbook also reveal that in 2010,

NOx

VOCs

Figure 3-8: Emissions of primary PM2.5 and precursor gases by sector in Tianjin, 2010


23

NOx

VOCs

Figure 3-9: Contributions of the industrial processes broken down by sub-sector in Tianjin, 2010

3.4.2 Emissions by Sector The energy sector ( coal-fired power generation ) is

In sub-sectioning the industrial processes sector of

the biggest contributor to PM2.5 pollution ( including

Tianjin, we have found that production of oil products,

primary emissions and secondary transformations ) in

cement and iron and steel are the major sources of

Tianjin. In 2010, the energy sector discharged 83% of

precursor gases.

the city's SO2 and 64% of NOx, becoming the biggest emitter in both of the precursors, whereas in Beijing, NOx from the transportation sector is the major source of emissions.


24

3.4.3 Emissions by Fuel Type

Tianjin is similar to Beijing in source composition for NH3, but is 13% higher than Beijing in the VOCs

In terms of fuel, coal is the dominant source for SO2

emitted through burning of biogenic substances ( e.g.

and NOx in Tianjin, accounting for 91% and 61% of

combustion of straws ). According to a survey â‘  on

the two respectively ( Figure 3-10 ).

the urbanization rate in Chinese cities, the respective rate of Beijing is 18% higher than that of Tianjin in 2010.

NOx

VOCs

Figure 3-10: Emissions of primary PM2.5 and precursor gases by fuel type in Tianjin, 2010

â‘  http://www.ciudsrc.com/bbs/viewthread.php?tid=8805


25

3.5 Source Analysis of PM2.5 in Hebei

3.5.1 Total Emissions

Both chemical composition and sector contributions

(Mts) of primary fine particles and precursor gases,

suggest that the main source of PM2.5 in Hebei is coal,

which are six times that of Tianjin and seven times

a result similar to that of Tianjin. Broadly speaking,

that of Beijing. The pollutants can be ranked in order

the energy sector ( coal-fired power generation ),

of size of emissions: SO2 ( 2.7 Mts ), NOx ( 1.7 Mts ),

industrial production and residential and commercial

VOCs ( 1.4 Mts ), primary PM2.5 ( 1.3 Mts ) and NH3

sectors are the top three contributors of PM2.5.

( 1 Mts ).

In 2010, Hebei emitted a total of eight million tons

NOx

VOCs

Figure 3-11: Emissions of primary PM2.5 and precursors gases by sector in Hebei, 2010


26

3.5.2 Emissions by Sector

The contribution of industrial processes sector ( e.g. production of cement, lime, coke and bricks ) to

The energy sector ( coal-fired power generation )

Hebei’s emissions is far greater than that in Beijing

is the largest contributor to primary pollutants and

and Tianjin. Take NOx as an example, NOx emissions

precursors in Hebei, accounting for 67% and 46% of

from industrial processes are equivalent to that of the

SO2 and NOx respectively. Statistics from the China

transportation sector, accounting for 20% of the total

Electric Power Association indicates that in 2011,

NOx and higher than that of Beijing ( 9% ) and Tianjin

there were a total of 153 coal-fired power plants in

( 13% ). Moreover, industry processes is also the

Hebei, which make up 73% of the installed capacity

major source of primary PM2.5 emissions in Hebei

of thermal power generation in Beijing, Tianjin

( 50% of the total ).

and Hebei combined. Coal used in thermal power generation is the main source of SO2 emissions.

We have found that the iron and steel sector is

Coal-based heating for households and businesses

the biggest industrial contributor to primary and

has contributed an additional 16% of SO2 emission.

secondary emissions of PM2.5 in Hebei, followed by cement production.

NOx

VOCs

Figure 3-12: Contributions of the industrial processes sector broken down by sub-sector in Hebei, 2010


27

3.5.3 Emissions by Fuel Type Agriculture plays a greater role in Hebei's economy Coal is the most important fuel type in Hebei. Similar

than in Beijing or Tianjin. This is reflected in fuel

to Beijing and Tianjin, coal burning in Hebei is the

composition of VOCs emissions in Figure 3-13, which

top contributor to SO2 emissions, accounting for 81%

indicates that 24% of VOCs comes from biogenic

of the total discharge. Coal burning also contributes

sources such as agricultural residues, fugitive

to 24% of the directly emitted PM2.5 and 45% of the

methane emissions and fuel wood combustion.

precursor gas NOx.

NOx

VOCs

Figure 3-13: Emissions of primary PM2.5 and precursor gases by fuel type in Hebei, 2010


28


29

PM2.5 Concentrations under a Business-as-usual Scenario


30 PM2.5 Concentrations under a Business-as-usual Scenario A business-as-usual scenario predicts the trend of

time-dependent. PM2.5 in this report refers to annual

fine particles emissions according to the current

average concentration. There are no official statistics

status, excluding possible further interventions. This

regarding annual average PM2.5 concentrations

chapter presents results from simulations of the

in 2010 in Jingjinji, but we managed to produce

current PM2.5 concentrations and predicts the trend

estimates based on the comparison between the

for the next 20 years.

model outputs and the actually measured values.

4.1 Estimation of Current PM2.5 Concentrations

The following is a description of methods often used in the scientific literature to determine PM2.5 concentrations:

The concentration of PM2.5 is highly regional and

Assume a fixed conversion rate between PM10 and PM2.5 and deduce the concentration of the [1]

latter based on the measured concentration of the former. The MEP has applied such a method by assuming a fixed conversion ratio of 0.65 ( see Table 4-1 ). While this method is simple, the estimates are subject to a high degree of uncertainty ( Brauer et al., 2012 ). From instrumental measurements. This method can accurately measure the PM2.5 concentration

[2]

in a given area, but the geographic distribution of monitoring stations is heavily biased. Only from late 2012, key regions in China including Jingjinji began to publish real-time PM2.5 monitoring data. Estimates from air quality models, including the CMAQ and GAINS models used in this research.

[3]

Simulation results are sensitive to emission inventory and other factors, thus errors are expected compared to the actual measurements. Satellite-derived PM2.5. This method is based on satellite observation of Aerosol Optical Depth

[4]

( AOD ) to calculate ground-level concentrations of PM2.5. The AOD data provided by NASA is only partially accessible to the public. The latest AOD data gives average PM2.5 concentrations from 2001 to 2006 â‘  .

â‘  Please see: http://www.nasa.gov/topics/earth/features/health-sapping.html


31

Table 4-1: PM2.5 concentrations in 2010 estimated from PM10 by the MEP ( µg/m3 )

PM10 concentration

Estimated PM2.5 concentration

Beijing

124

80.6

Tianjin

105

68.25

Shijiazhuang, Hebei

105

68.25

This research integrates the four methods above by verifying the simulated results from CMAQ against actual measured values ( where possible ) and satellite-derived values. In Table 4-2, we present PM2.5 concentration weighted by population ① and the concentration averaged over urban areas ( 20 km x 20 km ). The detailed results for Beijing, Tianjin and Hebei are summarized in the same table.

Table 4-2: Average PM2.5 concentrations in Beijing, Tianjin and Hebei, 2010 ( µg/m3 )

PM2.5 concentration ( population-weighted )

Data sources

PM2.5 concentration ( urban average, 20 km x 20 km )

Data sources

Beijing

73.7

Brauer et al. ( 2012 )

89.2

Observed values from Beijing Environ. Monitoring Center & U.S. Embassy in China

Tianjin

76.0

CMAQ simulation

91.7

CMAQ results for urban Tianjin

Hebei

80.1

CMAQ simulation

112.2

CMAQ results for urban Shijiazhuang

① The population-weighted mean concentration is acquired by multiplying the PM2.5 concentration in each grid

cell with the respective population ratio, and summing up over all grids.


32

In the table above, the average weighted by population and urban average in Tianjin and Hebei come from the simulation results of the CMAQ

4.2 Trends of PM2.5 concentrations in 2010-2030

modeling. Compared to the actual measurements,

With the GAINS model provided by IIASA, we

the simulation results for Beijing appear too high

have made predictions of fine particles ( PM2.5 )

( GAINS results ) or too low ( CMAQ results ).

concentration trends from 2010 to 2030 in Beijing,

In view of that, when calculating the population-

Tianjin and Hebei under the business-as-usual

weighted mean concentration for Beijing, we have

scenario.

adopted the value from Brauer et al. ( 2012 ), a research project contributing to the Global Burden

In predicting fine particles concentrations, we need to

of Disease 2010 Project. In terms of urban average

make assumptions on future economic development,

concentration in Beijing, we have referred to the

population growth, energy demand and supply and

released measurements from the Beijing Municipal

other factors. The GAINS model has four built-

Environmental Monitoring Center and the U.S.

in baseline scenarios ( UNEP, IEA, ECLIPSE and

Embassy in Beijing.

TOP, see section 2.2 for more details ). Because the assumptions for each of these scenarios vary, their results on the trends of PM2.5 concentrations can differ. Attaching equal importance to the results from the above four baseline scenarios, we have predicted the average trends of PM2.5 concentrations from 2010 to 2030 in Beijing, Tianjin and Hebei ( see Figure 4-1 ).


33

PM2.5

Figure 4-1: Average trends of PM2.5 concentration change during 2010-2030 in a business-as-usual scenario

Figure 4-1 shows that without further interventions,

Clearly, we cannot rely solely on the business-as-

the concentration of PM2.5 will decline gradually.

usual scenario to reach the national target of reducing

Beijing will witness a drop of 10% in PM2.5

the PM2.5 concentration level by 25% by 2017. Nor will

concentration in 2030 compared with the 2010

this non-intervention scenario take us to the National

level. The drops in Tianjin and Hebei are 13% and

II Standard, reducing fine particle concentration to

11% respectively. This decreasing trend is due to

35Âľg/m3 in the next two decades. Therefore, we must

the increasing role of the service industry in the

commit to doing more. The State Council has recently

economy, the growth of clean energy as spelled out

released its action plan for air pollution prevention

in the national energy planning and the expectation

and control. This marks a great leap forward in terms

that outdated production capacity will be shut down or

of escalating intervention efforts in addition to those

eliminated in the Five Year Plan.

assumed in the business-as-usual scenario.


34


35

Strategies to Achieve PM2.5 Standards in Jingjinji


36 Strategies to Achieve PM2.5 Standards in Jingjinji The most severe air pollution in China is found in Beijing, Tianjin and Hebei, and curbing PM2.5 levels is high on the government agenda. According to

5.1 Reduction Potentials of Emission Policy Measures

the Atmospheric Pollution Prevention and Control

A comprehensive policy package for emission

Action Plan issued by the State Council in September

reduction requires a careful policy evaluation on the

2013, by 2017, the PM2.5 concentration in Jingjinji

possible policy measures. Based on our analysis on

shall be reduced by 25%, and the annual average

sector apportionment and review of relevant literature

concentration of PM2.5 in Beijing shall be kept below

( e.g., Liu et al., 2013 ), we have identified 79

3

60 µg/m . In an implementation plan endorsed

measures that have high potential for reducing PM2.5.

by six ministries, a bold proposal to curb coal

These measures are distributed across domestic

consumption has been put forward for the first time.

sector, industrial combustion sector, industrial

By 2017, Beijing will reduce 13 million tons of coal

process, power plants and transport sector ( see

consumption, Tianjin 10 million tons, and Hebei 40

Appendix 3 for a complete list ). If we were to apply all

million tons.

these 79 measures in Jingjinji to achieve the 35µg/m3 target by 2022, this would mean a reduction in total

Current policies call for a 25% reduction in PM2.5

PM2.5 emissions of 50% by 2017, and furthermore, a

level, but this is still far from reaching the National

reduction of 80% of the directly emitted PM2.5, 60% of

3

II Air Quality Standards ( 35 µg/m ). A substantial

SO2, 75% of NOx, 85% of NH3 and 90% of VOCs by

gap exists between the status quo and public

2022.

expectations, and moreover, current measures have significant limitations. With the aim of achieving 3

For Beijing, Liu et al. ( 2013 ) have conducted

the 35µg/m target in the coming decade, we have

sensitivity analysis ( in Figure 5-1 ) on the emission

proposed an alternative policy scheme in the following

reduction potentials of key measures ( either as single

sections.

measure or combination of several measures ). They show that more active measures can reduce primary PM2.5 emission in Beijing by over half by 2022.


37 NOx

EE stands for energy efficiency, HDDV for heavy-duty diesel vehicles, LDDV for low-duty diesel vehicles and CFPP for coal-fired power plants. ( Liu et al., 2013 )

Figure 5-1: Top 15 measures in Beijing by the percentage of pollutants that can be reduced from its total emissions after applying each measure


38

Although we lack similar sensitivity analysis of policy

electricity supply from external sources. However, for

measures at the Jingjinji regional level, this Beijing-

Hebei this would mean an increase in the percentage

focused study ( Liu et al., 2013 ) is highly relevant

of renewable energy in its energy mix. Similarly,

and instrumental in reducing PM2.5 emission in

shutting down iron and steel plants would be much

Tianjin and Hebei. One justification is that Beijing

more difficult in Hebei than that in Beijing.

enjoys considerable technological advantages over Tianjin and Hebei. If these emission measures were

We emphasize that substantial financial and policy

to be applied in Hebei and Tianjin, we could expect

support are needed to achieve the 2022 goals. This

greater reduction potentials to be achieved there

report evaluates policy measures primarily from the

as well. Nevertheless, the extent to which each

perspective of technical reduction potentials, with no

measure reduces emission will vary by city due to

consideration given to financial or economic matters.

different PM2.5 compositions. Both the design and the

Some of the proposed measures could have social

implementation of policy measures have to be tailored

implications. For instance, if the energy demands in

to local conditions. For instance, the measure of “85%

the Jingjinji region are met by shutting down power

electricity from outside� in Figure 5-1, in essence,

plants in the region and transmitting electricity from

is about the reduction of electricity generated by

elsewhere, then we are not solving the problem, but

coal-fired power plants. For Beijing, owing to its

rather shifting the pollution from one place to another.

own resource constraints, this would mean seeking


39

5.2 A Pathway to Blue Skies If the Jingjinji region is to meet National Air Quality Specification Level II ( 35 Âľg/m3 ), it should focus on the following areas:

Limit the use of coal, especially by utility industry, and halt the building of any new [1]

coal-fired power plants. Instead, the region should replace coal-fired power generation by renewable energy.

[2]

Shut down and rectify pollution and energy-intensive industries such as cement plants, and iron and steel plants, and replace coal-fired boilers with gas-fired ones.

Upgrade existing small-scale boilers for domestic and commercial use by replacing [3]

coal-fired boilers with gas-fired ones, increase the proportion of gas consumption in the domestic sector, and ban agricultural waste incineration.

[4]

Improve the quality of oil products and emission standards for vehicles.


40

Based on our analysis of sector apportionment and evaluation of policy measures, we put forward these targeted suggestions for Beijing, Tianjin and Hebei to achieve PM2.5 standards by 2022:

Beijing Industrial processes are the major source of primary PM2.5, and the precursor gases ( SO2, NOx and VOCs ) are mainly emitted by the energy and transport sectors, thus these three sectors should become the main targets. In terms of emissions by fuel type, combustion of coal and oil products and non-fuel emissions ( emissions during various industrial processes ) are the major sources of gaseous precursors. Emission measures should concentrate on substantial reduction of coal use, improving quality of oil products and raising the emission standards for vehicles.

Actions that should be put into place by 2022 include:

[1] Shut down coal-fired power plants within the

[4] Escalate the adoption of end-of-pipe solutions

capital boundary, increase the percentage of

in the electric power sector. Install flue gas

electricity from renewable sources in total energy

desulfurization and denitration equipment for all fossil

mix, and boost the development of distributed solar

fuel power plants. Use low-NOx burners and install

and wind power. Source renewable energy from the

fabric filters.

surrounding area. [5] Reduce the emission of volatile organic [2] Shut down all existing iron and steel plants and most cement plants. Install fabric filters in cement kilns and cease the building of new cement plants. [3] Apply the National VI Emission Standard to lightduty gasoline cars and heavy-duty diesel cars, and increase the percentage of buses and cabs fueled by clean energy to over 40%.

compounds ( VOCs ) from industrial processes.


41

Tianjin The energy sector ( coal-fired power plants ) is the biggest source of PM2.5 emissions in Tianjin. In terms of fuel contribution, emission from coal burning and non-fuel escapement ( fugitive gases escaped from industrial processes ) play the main role. Efforts should be targeting reducing fuel consumption and fugitive emissions during the production of oil products.

Actions that should be put into place by 2022 include:

[1] Increase the use of wind power and distributed

[3] Shut down the most polluting cement and steel

solar power and their shares in the energy mix, and

plants, install fabric filters in existing cement kilns,

significantly reduce the percentage of coal-fired

and ban new cement and steel plants.

power in energy provision. [4] Expedite the adoption of end-of-pipe technologies [2] Tighten the control of fugitive emissions during

in the electric power sector, realize simultaneous

production of oil and oil-related products, impose

desulfurization and denitration of flue gas from all

desulfurization measures, and reduce the emission of

fossil fuel power plants, use low-NOx burners, install

VOCs.

fabric filters, and shut down part of the existing coalfired power plants.


42

Hebei The energy sector ( coal-fired power plants ) is the biggest PM2.5 emitters among all industrial sectors in the Hebei province. The industrial processes sector is the main source of primary PM2.5 aerosol, however domestic and commercial sectors also contribute significantly. In terms of emissions by fuel combustion/use, mitigation efforts should focus on the two main fuel types, namely coal and non-fuel ( from industrial processes sector ).

Actions to put in place by 2022 include:

[1] Invest heavily in renewable power generation and

the electric power sector with the goal of installing

use them to replace coal-fired power plants.

flue gas desulfurization and de-nitration technology in all fuel-fired power plants. Use low-NOx burners,

[2] Accelerate the shutdown of the most polluting iron

install fabric filters, and shut down part of the existing

and steel plants, coking plants and cement plants,

coal-fired power plants.

and sectors plagued by over-capacity problems. Install flue gas desulfurization equipment and fabric

[4] Upgrade existing small-scale boilers for domestic

filters in iron and steel plants, and install fabric filters

and commercial use by substituting gas-fired

in cement kilns.

boilers for coal-fired ones, use more gas instead of coal as household fuel, and ban agricultural

[3] Expedite the adoption of end-of-pipe solutions in

waste incineration.


43

A breakdown of total emissions by sub-region reveals

own, they should also build regional mechanisms

Hebei emits the largest amount of pollutants. This

of air pollution prevention and control as soon as

is because it hosts the largest part of the heavy

possible. Hebei province should be given priority in

industries, and also because the impacts of small-

terms of allocating resources. Ultimately, effective

scale boilers for residential and commercial purposes

control of PM2.5 emissions has to integrate a full set

are greatest in this region. If Hebei fails to reduce the

of policy measures. These include reducing coal use

emissions of PM2.5 and precursor gases in a timely

in energy provision, strict enforcement of end-of-

and effective manner, the efforts to abate air pollution

pipe technology, upgrading small-scale boilers, and

of the entire region will be undermined. While Beijing,

implementing regional air pollution monitoring and

Tianjin and Hebei are tackling air pollution on their

early warning and emergency response systems.


44


45

Conclusions


46 Conclusions

Based on the sector-specific and fuel-specific

to reduce total emissions by over 80%, Beijing,

emission inventory embedded in the GAINS model,

Tianjin and Hebei have to fundamentally adjust their

this research simulates PM2.5 concentrations in

industrial structure and energy mix, substantially

Beijing, Tianjin and Hebei, using the CMAQ air quality

decrease the ratio of heavy industries, replace

model. We have also analyzed the reduction potential

coal with clean energy in the electric power sector

of various policy measures targeted at achieving

and adopt efficient and coordinated end-of-pipe

3

35Âľg/m PM2.5 level by 2022.

technologies.

PM2.5 emissions in Beijing, Tianjin and Hebei mainly

Considering PM2.5 precursors such as SO2 can travel

come from coal-fired power generation, industrial

far away from their sources, and moving out coal

production, combustions of small-scale boilers

power plants may deteriorate pollution elsewhere,

for commercial and domestic purposes and the

Beijing, Tianjin and Hebei should not depend on

transportation sector. In total, these sectors emit

outsourcing coal-fired power plants to other provinces

over 12 million tons of primary PM2.5 and precursor

or importing dirty electricity from these provinces.

gases such as SO2, NOx, NH3 and volatile organic

Instead, the Jingjinji region should promote the

compounds ( VOCs ) in 2010. If aggressive

development of clean and renewable energy to

measures are not adopted and reduction efforts

control and decrease the overall use of coal.

stay the same as those preceding the release of the Action Plan by the State Council, PM2.5 in 2030 will

This report provides an important foundation for a

remain unacceptably high. In order to reduce PM2.5

science-based, systematic approach to tackling PM2.5

3

concentration to 35 Âľg/m in ten years, or equivalently

pollution in the Jingjinji region.


47

Appendices


48


49 Appendix 1: PM2.5 Background Information PM2.5 is not a single pollutant, but rather a compound of pollutants existing in various forms: filterable, condensable, organic, inorganic, solid, and gas. PM2.5 sources can be classified into primary and secondary sources. See the following chart:

Secondary

Precursors SO2 NOx NH3 VOCs

Transformation

SO42NO3NH4+

SNA

OC

OC = Organic Carbon CB = Carbon Black

BC = Black Carbon EC = Elementary Carbon

Source: Hu ( 2012 ) and Environment Canada ( 2001 )

PM2.5

Primary Crust elements Metal BC/EC/CB

Primary and Secondary PM2.5 Sources

Primary PM2.5 refers to tiny solids or liquid droplets

nitrogen ( NOx ), ammonia ( NH3 ) and various

released either directly into the air from a variety of

hydrocarbons referred to as volatile organic compounds

sources such as cars, trucks, factories, construction

( VOCs ). These gases can result from fuel combustion

sites, agriculture, unpaved roads, stone crushing, and

in motor vehicles, at power plants, and in other

burning of wood.

industrial processes.

Secondary PM2.5 is formed in the air from the chemical

Much of the available research focuses on primary

change of gases, or indirectly formed when gases from

sources of PM2.5. Analysis of PM2.5 transformed from

burning fuels react with sunlight and water vapour.

precursors has been lacking. Our research shows that

Precursor gases involved in secondary formation

it is important to consider both of these sources.

include sulphur dioxide ( SO2 ), oxides of


50 Appendix 2: The Chemical Composition of PM2.5

The figure on the left shows the percentages of Sulfate-Nitrate-Ammonium ( SNA ), Nitrate ( NH4+ ), Black Carbon ( BC ) and Organic Carbon ( OC ) in the mass concentration of PM2.5 ( e.g. if the figure shows the SNA in a certain area is 0.4, it means that SNA accounts for 40% of PM2.5 mass concentration in this area ). SNA is the dominant component in PM2.5 in the Jingjinji region, accounting for 50%70% of the total mass concentration. BC is mainly caused by incomplete combustion of fossil fuels or biomass, and is usually regarded as an indicator for measuring pollution caused by vehicles ( Huang et al. 2006 ). OC belongs both to primary and secondary pollutants. Its sources include primary organic aerosol ( POA ) generated through fuel combustion and secondary organic aerosol ( SOA ) transformed from direct VOCs emissions.


51 Appendix 3: A List of Sector-specific Emission Reduction Measures Table A1: List of sector-specific emission reduction measures

Sector

Emission reduction measures

1.Improvement of energy efficiency in the domestic sector 2.Substitution of district heating for a decentralized heat-supply 3.Phasing out of residential coal stove Domestic

4.Growth of natural gas consumption in the domestic sector 5.Promotion of low-sulfur coal in the domestic sector 6.Installation of web scrubbers in residential coal-fired boilers 7.Installation of low-nitrogen burner ( LNB ) in residential gas-fired boilers

1.Increasing energy efficiency in the industrial-combustion sector 2.Promotion of combined heat and electricity generation 3.Phasing out of small capacity coal-fired industrial boilers 4.Substitution of gas-fired boilers for coal-fired ones Industrial combustion

5.Promotion of low-sulfur coal in the industrial combustion sector 6.Installation of flue gas desulfurization ( FGD ) in coal-fired industrial boilers 7.Installation of FF in newly-built coal-fired industrial boilers 8.Installation of LNB in industrial boilers 9.Installation of wet scrubbers in old industrial boilers 10.Installation of fabric filter ( FF ) in existing coal-fired industrial boilers


52

Sector

Emission reduction measures

1.Increased use of electricity generated from power plants outside the city boundary 2.Phasing out of small capacity coal power plants 3.Substitution of natural gas-fired power plants for coal-fired ones 4.Substitution of IGCC for traditional coal-fired power plants 5.Substitution of natural gas-fired heating plants for coal fired ones 6.Installation of carbon capture and storage technologies 7.Promotion of low-sulfur coal in power plants Power plants

8.Installation of FGD in coal-fired power plants 9.Installation of FF in newly-built coal power plants 10.Installation of electrostatic precipitators ( ESP ) & FF in newly-built coal power plants 11.Installation of FF in old coal-fired power plants 12.Installation of LNB in power plants 13.Installation of selective-catalytic-reduction ( SCR ) in newly-built gas-fired power plants 14.Installation of SCR in all existing power plants


53

Sector

Emission reduction measures

1. National IV Standard for light vehicles 2. National IV Standard for heavy duty diesel vehicles ( HDDVs ) 3. National V Standard for HDDVs 4. National III Standard for motorcycles 5. National II Standard for non-road vehicles ( ORVs ) 6. National IV Standard for gas vehicles 7. National V Standard for light duty gasoline vehicles ( LDGVs ) 8. National VI Standard for LDGVs 9. National VI Standard for HDDVs 10. National III Standard for ORVs 11. National IV Standard for ORVs 12. National V Standard for ORVs Transportation

13. National VI Standard for ORVs 14. Scrap pre-National I gasoline vehicles 15. Scrap pre-National I diesel vehicles 16. Scrap national I gasoline vehicles 17. Scrap national I diesel vehicles 18. Scrap national II gasoline vehicles 19. Scrap national II diesel vehicles 20. Restriction on the number of private cars 21. Promotion of gas vehicles 22. Promotion of alternative energy buses 23. Promotion of alternative energy taxis 24. Promotion of alternative energy private cars 25. Installation of selective-catalytic-reduction in diesel vehicles 26. Recycling of oil vapor in gas stations


54

Sector

Emission reduction measures

1. Shutdown of cement plants with vertical kilns and non-precalciner rotary kilns 2. Shutdown of coke plant old ovens 3. Ban newly built/renovated/expanded cement plants 4. Ban newly built/renovated/expanded lime plants 5. Ban newly built/renovated/expanded brick plants 6. Ban newly built/renovated/expanded glass plants 7. Ban newly built/renovated/expanded iron and steel plants 8. Ban newly built/renovated/expanded coke plants 9. Shutdown of some existing cement plants 10. Shutdown of some existing lime plants Industrial process

11. Shutdown of some existing tile and brick plants 12. Shutdown of some existing glass plants 13. Shutdown of some existing iron and steel plants 14. Shutdown of some existing coke plants 15. Substitution of gas-fired kilns for coal-fired ones 16. Installation of FF in cement plants 17. Installation of SNCR-DeNOx in precalciner kilns of cement plants 18. Installation of FF in lime plants 19. Installation of end-of-pipe particle control in sinter plants 20. Stricter control of fugitive emissions in sinter plants 21. Installation of end-of-pipe SO2 control in sinter plants 22. Adoption of low-VOC materials in the coating industry

Source: Liu et al. (2013)


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Tracing back the smog