Network perspective of embodied PM2.5 – A case study

Journal of Cleaner Production xxx (2016) 1e10

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Network perspective of embodied PM2.5 e A case study Muhammad Wakeel a, Siyuan Yang a, Bin Chen a, b, *, Tasawar Hayat b, c, Ahmed Alsaedi b, Bashir Ahmad b a

State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China NAAM Group, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia c Department of Mathematics, Quaid-i-Azam University, 45320, Islamabad, Pakistan b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 3 September 2016 Received in revised form 13 October 2016 Accepted 24 October 2016 Available online xxx

Economic growth due to social activities such as industrialization, urbanization, population growth, and transportation in countries like India is coupling with an increase in PM2.5 emissions, which are main contributors to air pollution. In this paper, the embodied PM2.5 emissions generated from economic activities in India were investigated. We quantified the PM2.5 emissions from each economic sector based on input-output analysis (IOA). Also, the control relations within these economic sectors were identified based on ecological network analysis (ENA) to determine the dominance and dependency of each sector. The results showed that 86% (4858 kt) of total PM2.5 emissions were direct and 14% (812 kt) were indirect in 2010. Furthermore, ENA-based results represented the pairwise control relationships among sectors by finding that “Sale, maintenance, and repair of motor vehicles, retail sale of fuel”, “Chemicals and chemical products”, “Smelting of nonmetal mineral products” and “Ordinary & special machinery & equipment” were the dominant sectors that controlled the flow of embodied PM2.5 emissions from other sectors such as “Construction”, “Agriculture”, “Mining and dressing”, “Catering services”, and “Waste manufacturing and recycling”. It is suggested that ENA be adopted as a more systematic approach to uncover PM2.5 flow pattern among the economic sectors, as compared to traditional methods. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Air pollution Embodied PM2.5 emissions inventory Network control analysis Input-output analysis

1. Introduction In recent years, socioeconomic activities in India have increased to dramatic levels because of rapid industrialization, urbanization, population growth, and transportation, which in turn, increased emissions that critically affected the atmospheric environment, especially with regards to contamination by atmospheric particulate matter (PM) (Correia et al., 2013; Yin and Harrison, 2008; Singh et al., 2014; Das et al., 2015). Fine particulate matter (PM2.5), which includes particles less than 2.5 mm in size, is produced by either incomplete combustion of fossil fuels and biomass or by various reactions of gaseous pollutants, such as oxides of nitrogen (NOx), SO2, and volatile organic compounds (VOCs) (Zhang et al., 2012; Pope et al., 2009). Many cities in India have remarkably higher levels of PM2.5 than the upper limit of the WHO standard level (WHO, 2013) as shown in Fig. 1, leading to questions of how

* Corresponding author. State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China. E-mail address: [email protected] (B. Chen).

socioeconomic activities affect PM2.5 and how to maintain future economic activities with minimum output of PM2.5 emissions, which is compulsory for stable pollution management and clean environment. The reduction or control of PM2.5 emissions in any country is possible only by the identification of PM sources, which effectively helps in developing mitigation strategies. Source apportionment is an important task in air pollution management and control, which could lead to the implementation of better policy options in controlling concentration levels (Begum et al., 2010). Various studies worldwide related to airborne PM have focused either on “Production-based emissions accounting” or “Consumption-based emissions accounting” (Zhang et al., 2015; Tao et al., 2014; Yang et al., 2015; Streets et al., 2006). Production-based emissions accounting is based mostly on chemical and technological approaches that are the most effective ways to trace back to the sources that are main producers of PM2.5. For example, in India, various studies were done from a production-based perspective with conclusions that the main sources of PM2.5 emissions are biomass burning, transportation, and coal combustion (Police et al., 2016; Karar and Gupta, 2007; Das et al., 2015; Joon et al., 2011; Sahu et al., 2011; Dey

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Fig. 1. Most polluted cities by PM2.5 emissions. Note: India (IN); Pakistan (PK); Bangladesh (BH); China (CN); Russia (RU); France (FR); United- –Kingdom (UK); Qatar (QR). Source: WHO, 2013.

et al., 2012). Consumption-based emissions accounting (during supply and final consumption of goods) provides alternative ways to understand the fundamental causes of emissions discharge. It was first introduced by Wiedmann et al. (2007) and further utilized by other scientists to calculate CO2 emissions for consumers (Minx et al., 2009; Davis and Caldeira, 2010). Hertwich and Peters, (2009) quantified air pollutants associated with the final consumption of goods and services in 73 nations and found that 72% of emissions were related to household consumption, 18% to investments, and 10% to government consumption. Zhao et al. (2015) focused on interprovincial virtual air pollution in China with the help of a consumption-based emissions framework. Guan et al. (2014) presented an interdisciplinary study in order to measure the magnitude of socioeconomic drivers of primary PM2.5 emissions in China between 1997 and 2010. However, in the existing literature, consumption-based emissions accounting studies related to India are scarce, although many studies related to production- and consumption-based emissions inventories have been done internationally. For example, Takahashi et al. (2014) estimated the production-based emissions and consumption-based emissions in Asia and found that China emitted 75% of production- and 77% of consumption-based total emissions. Huo et al. (2014) examined air pollutants in China by using production- and consumption-based accounting, concluding that 50% of air pollutants came from the machinery, equipment, device manufacturing, and construction sectors, while the service sectors were the lowest contributors of air pollutants. Some recent emissions accounting studies have borrowed the concept of “embodied emissions”, previously used for energy analysis (Li et al., 2016a). Embodied emissions are all the emissions created from “cradle to grave” of an item (Yohanis and Norton, 2002). For example, Jiang et al. (2016) examined the embodied mercury emissions in Beijing. Chen et al. (2013) investigated the embodied carbon dioxide emissions at global level. Yang et al. (2016) analyzed the embodied PM2.5 emissions in Beijing and found that smelting & press of metals, non-mineral products, and metal products had the highest control levels, whereas the catering service, agricultural sectors, and residential sectors were the lowest ranking sectors and the heavy industrial sectors were the main contributors of embodied emissions. Basically, embodied emissions are quantified with the help of bottom-up and top-down approaches. The bottom-up approach is data extensive, based on process analysis, which is why it deals from cradle to grave, and can be used to calculate the

environmental effect of any individual product (Wiedmann and Minx, 2008; Wakeel et al., 2016). However, it cannot explain the role of intermediate and final consumers (Zuhdi, 2015). The topdown approach completely relies on input-output analysis (IOA), which can describe the material flows in different economic sectors to quantify the amount of embodied emissions that are emitted during the manufacturing of products (Leontief, 1986). This method is constructed based on an economic input-output (I-O) table that shows inter-sectoral relationships in the form of monetary flow (Leontief et al., 1965; Leontief, 1970). Various researchers have quantified the embodied emissions flow at domestic, national, and global scales in the socioeconomic system via IOA. (Peters, 2008; Zhang et al., 2009; Miller and Blair, 2009; Zhao et al., 2011). Furthermore, some recent studies have introduced the concept of virtual material flow (Fang and Chen, 2015; Chen and Chen, 2013) that can be extended to calculate the inner interactions and linkages of embodied PM2.5 in a system. To analyze the inner interactions and linkages of embodied emissions within a system, a systematic approach is needed. Ecological network analysis (ENA), originally derived by Hannon (1973) from IOA, has been successfully applied to not only specific ecosystems (Schramski et al., 2006, 2007; Ulanowicz and Tuttle, 1992; Jørgensen et al., 2010; Chen and Chen, 2011; Jørgensen, 2016), but also complex socioeconomic systems. For example, Yang and Chen (2016) used ENA to investigate the dominant sectors and pathways of energy-water circulation and mutual relationship between pairwise components of wind power generation systems. Particularly, Yang and Chen (2016) first employed ENA to account regional PM2.5 emissions. Network control analysis (NCA) was another useful tool derived from ENA (Patten, 1978, 1982; Patten and Auble, 1981), which can be used to describe the control and dependency relationships underlying the PM2.5 emission flows among various economic sectors. In addition, cumulative pathways of emissions flows can be highlighted based on the allocation of integral control (Chen and Chen, 2012). However, studies in the existing literature related to embodied PM2.5 emissions in socioeconomic networks using ENA are still rare. The objective of this paper was to construct a PM2.5 emissions inventory framework at the sectoral scale, following the economic I-O model for India. The PM2.5 emissions generated from 16 sectors during economic activities were quantified. NCA derived from ENA was also employed to study the structure and function of each sector and to calculate the distribution of control level through the interactions between sectors. Furthermore, the contribution of each sector in the form of direct and indirect emissions was investigated by combining ENA with IOA. Finally, the dominant

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sectors were highlighted in the context of their linkages. The following sections described the detailed organization of the present study, Section 2 briefly explained the methodologies of IOA and ENA, and Section 3 presented the quantification of direct and indirect emissions, control analysis and a range of results. Discussion, future perspective and policy implications were given in Section 4. Finally, a range of conclusions were presented in Section 5.

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System of Air Quality and Weather Forecasting and Research (SAFAR), which was conceived, developed, and implemented by the Indian Institute of Tropical Meteorology (IITM, MoES) (Gufran et al., 2015). In addition, the Central Pollution Control Board (CPCB) also legislated daily limits of PM to line up with the international levels for PM10 and PM2.5 (MoEF, 2009).

2.2. Data sources 2. Material and methods 2.1. Study area India is the seventh-largest country in the world (see Fig. 2), with a total area of 3,287,263 km2 (IEB, 2012), and the world's second largest population of 1.32 billion, 72.2% of which living rurally and 27.8% living in urban and town agglomerations (WB, 2014). The concentration of PM2.5 in India is quite a serious issue, 80% of which is caused by biomass burning, fuel adulteration, vehicle emissions, and traffic congestion (WHO, 2011). According to the status and trends of national ambient air quality, more than 80% of Indian cities violate the WHO PM2.5 level (CPCB, 2014). For example, in Delhi, the average concentration of daily PM2.5 in 2014 was 153 mg/m3, which is more than four times higher than the standard level of PM2.5 concentration of 35 mg/m3 (TERI, 2015). In view of the current situation, the Indian Ministry of Earth Sciences (MoES) sponsored an ambitious project deemed the

We collected PM2.5 emissions data from 106 sectors with the help of a greenhouse gas-air pollution interaction and synergies (GAINS) model that was established by the International Institute for Applied System Analysis. The percentages of total embodied PM2.5 emissions in different regions are depicted in Fig. 3. The I-O table of 35 economic sectors was obtained from “the Statistical Bureau of India”. The national economy was divided into 35 economic sectors to compile urban I-O with emissions. These 35 economic sectors were further aggregated into 16 large sectors to understand the embodied emission flows within the system and identify the sectors with intensive PM2.5 emissions (see Table 1). The rest of the data was obtained from “India's Statistical Yearbook” and existing published reports in the literature (India Statistical Yearbook, 2007; WB, 2014). In addition, two aggregated sectors (Construction & Manufacturing) were removed from further calculations of direct and indirect emissions because their PM2.5 emission data were unavailable in the GAINS model.

Fig. 2. Map of study area.

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Fig. 3. Percentage of total embodied PM2.5 emissions from different regions of India.

Table 1 Compilation of economic sectors. 16 aggregated sectors

35 original sectors

Original sector number

Agriculture Mining Manufacturing

Farming, Forestry, Animal Husbandry, Fishery Mining & Quarrying Food, Beverages and Tobacco Textiles and Textile Products Leather, Leather and Footwear Rubber and Plastics Wood and Products of Wood and Cork Paper, Paper, Printing, and Publishing & Printing Petroleum Processing and Coking Chemicals and Chemical Products Nonmetal Mineral Products Rubber Basic Metals and Fabricated Metal Machinery, Nec Electrical and Optical Equipment Manufacturing, Nec; Recycling Electricity, Gas and Water Supply Construction Sale, Maintenance and Repair of Motor Vehicles and Motorcycles; Retail Sale of Fuel Wholesale Trade and Commission Trade, Except of Motor Vehicles and Motorcycles Retail Trade, Except of Motor Vehicles and Motorcycles; Repair of Household Goods Catering Services (Hotel and Restaurants) Transportation Transport Equipment Water Transport Air Transport Other Supporting and Auxiliary Transport Activities; Activities of Travel Agencies Post and Telecommunications Real Estate Activities Renting of M&Eq and Other Business Activities Public Admin and Defense; Compulsory Social Security Education Health and Social Work Other Community, Social and Personal Services Private Households with Employed Persons

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 20 31 32 33 34 35

Paper Industry Petroleum Processing Chemical Products Non minerals Metals Machinery & Equipment Manufacturing, Nec; Recycling Electricity & Water Construction Sale, Maintenance and Repair

Catering Services Transportation

Residential Services

2.3. Input-output analysis IOA is a useful top-down approach that can account embodied emissions or energy and material flows during the production and consumption of goods by relying on sectoral interactions and exchanges in a concerned system (Huang et al., 2016; Finnveden and Moberg, 2005; Li et al., 2016b; Chen et al., 2011). It is initially articulated by Leontief (1936, 1937) and further enhanced by various applications to environmental impact assessment (Leontief, 1970; Lenzen, 1998; Yu et al., 2013; Chen and Chen, 2015). The advantage of IOA is that it can account for both direct and indirect

environmental emissions, energy and water consumption during trade and services from interconnected economic sectors (Miller and Blair, 2009; Fang et al., 2014; Chen et al., 2016). However, before using IOA for embodied PM2.5 emissions, one has to accept several key assumptions, especially during the accounting of consumption-based PM2.5 emissions to set the system boundary and avoid double counting. For example, the value of the emission coefficient in both cases, whether goods and services are produced locally or imported from another country or region within the same country, is assumed to be identical (Wiedmann, 2009).

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xi ¼

n X

Xij þ yi

(1)

j¼1

where xi is the total economic output of the ith sectors, n refers to the number of economic sectors, Xij represents the monetary flows from the ith to the jth sectors, and yi is the final demand of sector i.

 A ¼ xij xj

(2)

x ¼ ðI  AÞ1 y

(3)

where x indicates the sectorial output, I is the identity matrix, A is the matrix coefficient, y refers to the final demand, and (I-A)1 is known as the Leontief inverse matrix.

ki ¼ Ei =xi

(4)

where ki is the ith PM2.5 emission coefficient, and Ei represents the total PM2.5 emissions from the ith sector.

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flow efficiency from sector j to sector i, fij is specific for embodied emissions flow from sector j to sector i, Tj indicates the total outflow from sector j. N is the dimensional integral flow matrix, I is the identify matrix, and nij represents the integral dimensionless value of gij, calculated with Leontief inverse matrix (Fath, 2004). G0 is the self-feedback matrix, which represents the flows that return to the same sector; G1 is the direct flow of embodied emissions; G2 refers to the flow between two sectors; and Gn is the indirect flow of embodied emissions. A dimensionless integral flow is computed as below:

. G0 ¼ gij0 ¼ fij Ti N0 ¼ ðn0 ijÞ þ G00 þ G01 þ G02 þ G03 þ …… þ G0n ¼ ð1  G0 Þ

(11) 1

(12) where G0 represents the backward-flow transfer efficiency, and N0 (n0 ij) is for the non-dimensional, input-oriented inter-component flows from j to i. We can make a system-based comparison between minute (fractional) transfers values with the help of control difference but control ratio (CRij) can be used to reveal the pairwise inter-sectoral comparisons.

Ei ¼ ki ðI  AÞ1 y

(5)

  Ei ¼ ðki y þ ki AyÞ þ ki A2 y þ ki A3 y þ …

(6)

CDij ¼ hij  hji

(13)

DPM ¼ kðI  AÞ1 y

(7)

.    max hij  hji CRij ¼ hij  hji

(14)

Ind PM ¼ kðI  AÞ1 y  DPM

(8)

where DPM and Ind PM indicate the direct and indirect PM2.5 emissions, respectively. Using Eqs. (7) and (8), one can calculate the direct and indirect PM2.5 emissions of each sector from the total emissions. 2.4. Ecological network analysis ENA can be performed to investigate interrelationships between the components of the system and the resulting hierarchy (Ulanowicz, 1972; Hannon, 1973). Network control analysis (NCA) is derived from ENA to describe the complete control and dependency relationships within ecosystem or sectors (Patten, 1978, 1982; Patten and Auble, 1981). In this respect, “control” is distributed among the system components, represented by the combination of their input/output environs. We used this concept for embodied PM2.5 emission accounting to determine how much of the emissions are controlled by different economic sectors. Here we modified ENA to calculate the material flows and degrees of control relationship between each economic sector in context of the embodied PM2.5 emissions. All the equations used for flow analysis and control analysis were adopted from existing studies in the literature based on pairwise integrals to quantify the degrees of control and dependency of each sector on the other sectors (Fath, 2004; Fath et al., 2007; Yang et al., 2016). Network flow analysis is similar to the IOA used to calculate the network-based control differences and ratios among sectors. Non-dimensional input-oriented inter-sectoral flows of embodied emissions are defined as:

. G ¼ gij ¼ fij Tj   N ¼ nij þ G0 þ G1 þ G2 þ G3 þ ……Gn ¼ ðI  GÞ1

(9) (10)

where G is the forward-flow transfer efficiency, gij represents the

where CDij is the control difference between sector j and sector i, the dependency and control levels among different sectors are represented by its positive and negative values, respectively. A positive value of CDij shows that sector j is dependent on sector i, whereas a negative value shows that sector j is controlled by sector i. CRij represents the control ratio between sector j and sector I, and its value lies between zero and one, which determines the direction of flow. If it has an absolute value of one, then flow is definite between sectors with dominant positions, which helps to differentiate the subordinate position of pairwise sectors. If the value is zero, there is a weak inter-sectoral relationship and the direction of flow is indistinct, creating a negative effect on mutual flow, either stopping or offsetting it. 3. Results It is evident from Fig. 4 that “Catering services” at the residential and commercial levels is the highest contributor of direct PM2.5 emissions, with 2274 kt, because of high combustion of biomass and agriculture residuals in cooking and heating stoves, followed by “Smelting process of nonmetal mineral product” and “Waste manufacturing and recycling” with 828 kt and 541 kt, respectively. Furthermore, the direct PM2.5 emissions from some other socioeconomic activities, such as “Agriculture” (21 kt), “Mining and quarrying” (11 kt), “Chemicals and chemical products” (8 kt), “Sale, maintenance and repair of motor vehicles and motorcycles” (7 kt), and “Papermaking & printing” (5 kt) were significantly small in comparison with earlier socioeconomic activities. In addition, the total amount of embodied PM2.5 emissions (direct and indirect) from these five sectors was only around 1% of the total emissions. “Electricity, gas and water supply,” with 217 kt, was the highest emitter of indirect PM2.5 emissions, followed by “Waste manufacturing and recycling” and “Catering services” at the residential and commercial levels, with 197 kt and 135 kt, respectively. Overall, the entire socioeconomic system in India contributed to the total embodied PM2.5 emissions (5670 kt), out of which 86%

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Fig. 4. Direct & indirect PM2.5 emissions from 16 aggregated sectors. Note: the sectors include Catering services at residential and commercial level (CAS); Smelting of nonmetal mineral products (NMP); Waste manufacturing and recycling (WMR); Ordinary & special machinery & equipment (OSE); Electricity, gas, and water supply (EGW); Other residential services (ORS); Transportation & storage (TRS); Petroleum processing & coking (PPC); Agriculture (AUG); Mining and dressing (MID); Chemicals and chemical products (CCP); Sale, maintenance, and repair of motor vehicles and motorcycles: retail sale of fuel (SMR); Papermaking & printing (PAP); and Other manufacturing services (OMR).

(4858 kt) were direct and 14% (812 kt) were indirect emissions. The proportions of direct to indirect PM2.5 emissions are depicted in Fig. 5. It is obvious that the “Agriculture” and “Waste manufacturing and recycling” sectors contributed the highest 95% of direct emissions, followed by “Catering services” at residential and commercial level (94%) and “Other residential services” (90%) because of the burning of fossils fuels and biomass at the residential and commercial levels. Meanwhile, “Mining and quarrying” (51%) had the largest proportion of indirect PM2.5 emissions, followed by “Electricity, gas and water supply” and “Papermaking & printing” with 48% and 46%, respectively, showing that the goods produced in these sectors were consumed in other sectors. The control ratios of embodied PM2.5 flow for each sector were also identified with NCA, which are important for determining the most sectoral domination for emissions as illustrated in Fig. 6. The value of control ratio from 0 to 1 is defined as the degree of dominance, while the negative value shows the extent of dependency. The magnitude of the control ratio from “Sale, maintenance and repair of motor vehicles and motorcycles, retail sale of fuel (SMR)” to “Agriculture” was 0.95, which indicated that the agricultural sector embodied PM2.5 emissions flow was controlled by the former. Overall, the pairwise control relationships analyses showed that “Sale, maintenance and repair of motor vehicles and motorcycles, retail sale of fuel” (SMR) was a dominant sector that controlled the flow of embodied PM2.5 emissions of other sectors such as, “Petroleum processing & coking” (PPC), “Agriculture”

(AUG), “Chemicals and Chemical products” (CCP), “Smelting of nonmetal mineral products” (NMP), and “Ordinary & special machinery &equipment” (OSE). Most of the flows from “Construction” (CON) and “Catering services” (CAS) to the rest sectors were presented in dark blue, indicating that these two sectors were subordinators of the other connected sectors. Pairwise relationships related to “Waste manufacturing and recycling” (WMR) were shown in green, which meant the dominance/dependency of this sector was not strong in terms of embodied PM2.5 flow pattern. In addition, to the dominant sectors, the stronger and weaker linkages among all sectors were identified. All the sectors, which had a control ratio of more than 0.5 or below 0.5, showed a strong linkage or relationship with other sectors. Strong linkages existed between the following pairwise sectors, MID-AUG, SMR-OSE, MIDBFM, SMR-EGW, and EGW-CCP and the weaker linkages, ranging from 0.5 to 0.5, existed between CAS-PPC, CAS-OMR, OSE-NMP, etc. Fig. 7 shows the dependent intensity of the 16 aggregated sectors based on NCA. It is evident that “Mining and dressing”, “Sale, maintenance and repair of motor vehicles, retail sale of fuel”, and “Electricity, gas, & water” were the main controllers of embodied PM2.5 emissions for the whole system. The other sectors including “Residential services”, “Construction”, and “Catering services” were below the horizontal line, which were dominated and controlled in the embodied PM2.5 emission network. The “Construction” sector depended greatly upon others sectors, and its dependency or

Fig. 5. Comparison of percentage of direct and indirect PM2.5 emissions from 16 aggregated sectors.

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Fig. 6. Pair-wise control/dependence ratios between each sector. Note: The control ratio relationships of each sector are indicated from the column to row. Positive numbers represent dominant position while negative numbers refer to the subordinate position. For example, from “PPC” to “MID” is dark blue, indicating that “MID” has a strong dominant position in their pairwise relationship. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 7. Control levels of 16 sectors within the whole system.

control intensity of “Transportation”, “Chemicals and chemical products”, “Waste manufacturing & recycling”, and “Papermaking & printing” fluctuated, indicating their positions in the system were not determined by control or dependency level.

4. Discussion and future perspectives PM2.5 emissions play a significant role in air pollution. PM2.5 emission accounting is based on either the production or consumption of goods and services. Production-based PM2.5 inventory is based on production processes, whereas consumption-based emissions accounting is an alternative method that is helpful in

identifying the impact of consumers on PM2.5 emissions. We adopted the term “embodied emission” that is extracted from the embodied energy concept as an alternative approach that deals with both production- and consumption-based PM2.5 emissions. Embodied PM2.5 emissions comprise both direct and indirect emissions (Li et al., 2015). Direct PM2.5 emissions are those emissions deemed on-site emissions during production processes for socioeconomic activities, whereas indirect PM2.5 emissions are those emissions that are produced during production processes of those items or products that are consumed in other sectors. For example, emissions produced by the “Electricity and water sectors” are called indirect emissions. Our results showed that “Catering

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services,” which are the most complex systems involving people and equipment at the residential and commercial levels, was a major source of embodied PM2.5 emissions in India because most of the Indian population (about 84%) relies on biomass as their primary fuel for cooking and heating stoves. Because of this high consumption of biomass for cooking and heating, stoves at residential and commercial sectors were the highest emitters of embodied PM2.5, which has also been found in previous studies. Joon et al. (2011) concluded that biomass burning in the residential and commercial sectors was a big contributor of PM.2.5 in India because biomass consumer stoves for cooking and heating emit 11,000 mg/m3 PM2.5 emissions, as compared to natural gas stoves with only 4.69 mg/m3 PM2.5 emissions. A similar study was done by Nerín et al. (2011), during which fine breathable particles (PM2.5) were measured as a marker of environmental smoke in the catering services, and the conclusion was that smoky areas had eight times higher levels of embodied PM2.5 than nonsmoking areas did. Lei et al. (2011) also concluded that “Catering services” at residential sector, with 80% emissions, is the largest emitter of embodied PM2.5 because of the high biomass consumption in cooking and heating stoves. Ezzati et al. (2004) found that pollutants released from biomass burning stoves are more than 10 times greater than those specified in the relevant standards are. In addition, two types of catering approaches called conventional and deferred are used most commonly in India. In the former, food is cooked and consumed at the same place (hotels, restaurants, homes, etc.), whereas in the deferred catering approach a central kitchen is used for cooking and food is supplied to different public places (hospitals, weddings, etc.) by light (van) and heavy (trucks) transport. Vehicles with diesel engines are additional significant sources of PM2.5 emissions, as diesel engines emit more PM2.5 than gasoline engines do (MECA, 2007). Moreover, this study differentiated between direct and indirect PM2.5 emissions, concluding that the agriculture sector led direct PM2.5 emissions with 95%, because of the great agricultural volume and consumption of various industrial products (fertilizer), petroleum products, and utilization of heavy transports for production and transportation of agricultural products. In addition, agricultural residuals are commonly used as biomass, especially in rural areas. The “Agriculture” sector was followed by the “Industrial” sector, with 94%, and then “Catering services” with 93%. “Mining and dressing” was the highest contributor of indirect PM2.5 emissions because most of machinery and petroleum products consumed in this sector are produced in other sectors and less direct emissions occur because India does not rely on domestic fossils fuels resources, which is why India's dependence on imported fossil fuels rose to 38% in 2012 (Sieminski, 2014). The “Construction” sector was excluded during calculations of direct and indirect PM2.5 emissions because of the unavailability of relevant data. Furthermore, ENA is a meaningful and systematic tool for uncovering the pattern of mutual relationships and control relationships underlying embodied PM2.5 emissions from among socioeconomic sectors. It can also be further extended to nexus studies of energy, water, carbon, and some other environmental pollutants including PM2.5 emissions (Chen and Lu, 2015; Chen, 2016; Chen and Chen, 2016; Fang and Chen, 2016; Wang and Chen, 2016). NCA is another useful tool of ENA for determining the dependence and control relationships between pairwise compartments. NCA based control ratios can also explain the strength of disparity between the two open-loop control magnitudes (Yang et al., 2016), which is helpful when comparing the I-O transfer ratio between two sectors. The magnitude of the control ratio explains the strong and weak linkages between pairwise sectors. In this study, the results based on NCA showed that “Mining and dressing”, “Sale, maintenance and repair of motor vehicles and

motorcycles; retail sale of fuel”, “Electricity, gas & water”, and “Agriculture” sectors were the main controllers of embodied PM2.5 emissions in the system. However, service sectors including “Residential services, “Other manufacture services,” and “Catering services” were below the horizontal line, which implied that service sectors were controlled by other sectors in the embodied emission network. The results based on NCA among sectors might be helpful in establishing some recommendations and policies to minimize the level of embodied PM2.5 emissions. 4.1. Emission control strategies Emission control strategies may be helpful for achieving national emission ceilings set for India, and effective in reducing human exposure to air pollution, in expediting air quality limit values, and in increasing protection of natural ecosystems. The main contributor of embodied PM2.5 emissions in India at the residential and commercial levels was catering services. This is due to the burning of agriculture residuals (biomass) in cooking and heating stoves and the extensively used transportation for catering services. We recommend the following as the most likely strategies to be effective in controlling PM2.5 emissions in India. Firstly, more than 80% of the Indian population relies on biomass as its primary fuel source for heating and cooking, which is a main contributor to PM2.5 emissions. PM2.5 emissions can be controlled by using alternative fuels and efficient technologies at the residential and commercial levels for cooking and heating food. For example, the Ruiru Youth Community Empowerment Program in Kenya has developed a lower polluting firewood-burning stove that is up to 60% more efficient than the open fires traditionally used in rural areas (UNEP Year Book., 2014). The Indian government could control the level of embodied PM2.5 emissions by introducing these types of cooking and heating stoves in the rural areas. Youth volunteers could be trained all over India to teach local women about the advantages of the new stoves and how to install them that might be a giant step to reduce embodied PM2.5level in various cities of India. Secondly, the heavy transportation network with diesel engines was another main contributor of PM2.5 emissions and NOx into the atmosphere during “catering services,” as diesel engines are important power systems for on-road and off-road vehicles because of their long record of reliability, high fuel-efficiency, high torque output, ease of repair, inexpensive operation, and extreme durability. PM2.5 emissions from transportation could be reduced by adopting cleaner vehicle technologies, emissions standards, an improved inspection program, and by replacing diesel engines with gasoline engines because gasoline engines emit less PM2.5 compared to diesel engines (MECA, 2007). Some additional technologies designed to control PM from diesel engines include diesel oxidation catalysts (DOCs), diesel particulate filters (DPFs), and closed crankcase ventilation (CCV). 5. Conclusion Industrialization, power generation, transportation, socioeconomic activities, and the residential and commercial sectors are main contributors to air pollution because of the combustion of fossils fuels and biomass from such. The present study revealed the embodied PM2.5 emissions, with the help of IOA and ENA. The results of this study strengthened the evidence that the use of biomass fuels/traditional stoves in residential and commercial sectors is the main source of embodied PM2.5 emissions in India. According to the results based on the IOA, 14% (812 kt) of total PM2.5 emissions were indirect in 2010 in India, an amount that is commonly neglected during policymaking or strategy development

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