Modelling the Current and Future Pollutant Emission from Non-Road Machinery: A Case Study in Shanghai

Received: February 14, 2023
Revised: May 11, 2023
Accepted: May 29, 2023

 Copyright The Author's institutions. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. 

Cite this article:

Chen, R., Yang, X., Xi, L., Zhen, H., Xiu, G. (2023). Modelling the Current and Future Pollutant Emission from Non-Road Machinery: A Case Study in Shanghai. Aerosol Air Qual. Res. 23, 230012. https://doi.org/10.4209/aaqr.230012

HIGHLIGHTS

• In-plant and harbor machinery were non-ignorable non-road mobile sources.
• Harbor machinery resulted in the local area presenting highest emissions.
• Forklifts were the main source of SO₂ emissions.
• The contribution of harbor machinery would grow up in 2025 compared with 2021.

Keywords: Emission inventory, Spatial distribution, Harbor machinery, Emission prediction, Port city

1 INTRODUCTION

Mobile source emissions have become one of the important issues of global air pollution, which account for about 80% of the deterioration of urban air quality (Baldasano, 2020; Tobias et al., 2020). Large amounts of pollutants and greenhouse gases such as sulfur dioxide (SO₂), nitrogen oxides (NO_x), hydrocarbons (HC), carbon monoxide (CO), particulate matter (PM), and carbon dioxide (CO₂), are emitted by mobile sources during the processes of fuel combustions (Li et al., 2020; Grassi et al., 2021). These pollutants cause adverse effects on air quality, human health, and climate change (Kodros et al., 2015; Li et al., 2020). For instance, VOCs (volatile organic compounds) and NO_x are the precursor pollutants for forming ozone and secondary aerosols (Sillman, 1995; Wu et al., 2016). In order to improve air quality, it is critical to take appropriate actions to control emissions from mobile sources.

Mobile sources generally include on-road mobile sources (motor vehicles) and non-road mobile sources (aircraft, ships, railway diesel locomotives, and non-road machinery). A bottom-up national emissions inventory—China Mobile Source Environmental Management Annual Report (2021) (MEE, 2021)—is derived by counting the number of motor vehicles and non-road mobile sources, presenting an increasing contribution of non-road sources to overall mobile source pollution. In 2020, the emissions of HC, NO_x, and PM from non-road mobile sources were 0.22, 0.76, and 3.48 times higher than those of motor vehicles. Notably, the total pollutant volume discharged from non-road machinery accounts for 67.20% of that from non-road sources, as suggested by the China Mobile Source Environmental Management Annual Report (2021). Non-road machinery had become a major contributor to pollutant emissions. Motor vehicle emissions have aroused much concern in China (Gao et al., 2020; Jiang et al., 2020). In recent years, China has been active in controlling road sources. National Emission Standards VI for light-duty vehicle control has been effective in 2020. However, less attention has been paid to non-road sources, especially non-road machinery. It is expected that by December 2022, the country will implement the National Emission Standards IV for non-road machinery. This indicates that the implementation of the standard for non-road machinery is slower than that for road vehicles. Shanghai government has promulgated registration and management measures in 2021 to screen non-road machinery. However, related subsequent emissions controls have not yet begun to be studied and enforced. In order to improve the air quality of Shanghai, it is necessary to strengthen the synergistic control of NO_x and VOCs. Considering the contribution of non-road machinery to air pollution, there is a critical need to study the emissions from non-road machinery in Shanghai.

To date, several studies have been conducted on non-road machinery emissions. For example, in the work of Guo et al. (2020), the pollutant emissions from non-road machinery, including agricultural and construction diesel machinery, have been systematically estimated in the Beijing-Tianjin-Hebei (BTH) region, China. The contribution of agricultural machinery can be slightly higher than that of construction machinery, accounting for 60–71% of the total emission. In addition, Zhang et al. (2020) have studied emissions characteristics of non-road mobile source pollutants in Tianjin, indicating the emissions from agricultural machinery for 62.90% of total emissions and construction machinery for 37.10%. From the previous studies, it is worth noting that non-road machinery is generally divided into agricultural and construction machineries. Nevertheless, in the work of Guo et al. (2020), the construction machinery has been zoned as many types of machinery for different applications, such as harbor machinery and in-plant machinery. The actual operating hours exceeded the estimated results by a large margin, making the pollutant emissions differ from the expected results. Also, in the study of Liu et al. (2021), non-road machinery has been classified into construction machinery, in-plant forklifts, and harbor machinery. Meanwhile, the emissions from in-plant forklifts in Shenzhen accounted for 17–24% of all types of machineries. The contribution of the emissions from forklifts in the factory, which is particularly important for the accurate identification of pollution source emissions, cannot be ignored.

Besides, numerous studies on the spatial resolution of the emissions have been conducted at the country level or province-levels such as Lang and Bian’s research (Lu et al., 2017; Bian et al., 2018; Ye et al., 2018; Zhang et al., 2020; Liu et al., 2021). By establishing the high temporal-spatial resolution air pollutant emission inventory for agricultural machinery, Lang et al. (2018) have found that high emissions can be distributed in northeast, north, and central-south China. Bian et al. (2018) used a bottom-up method to develop a non-road mobile machinery emission inventory for the year 2014 from Guangdong Province. The high agricultural machinery emissions have been found mainly distributed in the rural areas of the non-Pearl River Delta region. And the emissions for construction machinery can be concentrated in the Pearl River Delta region. Such emission studies for a large-scale area cannot satisfy the requirements of refining environmental management and the model simulation (e.g., CMAQ) (Community Multiscale Air Quality) (Wu et al., 2022). The study of Zhang et al. (2022) has narrowed the spatial resolution to the municipal level and the emission inventory of non-road machinery has been detailed discussed in Tianjin. Based on these results, targeted control policies have been proposed, which can be conducive to establish a cooperative mechanism for scientific research institutes, government departments, enterprises, and other relevant parties. Therefore, further studies are required for emission inventories at the county and township levels.

In addition, models such as CMAQ model required emission factors and activity levels to estimate emissions. Most studies have made measurements of emission factors of different machineries. Qu et al. (2015) have determined the emission factors of PM_2.5 in construction machinery under different working conditions. Ge et al. (2013) have shown the emission factors of pollutants of different types of combine harvesters during idling, walking, and operation. Moreover, the country has also given the recommended values of emission factors in the guidelines, indicating that the existing emission factors are improved. However, the activity levels of non-road machinery lack studies and would change greatly from one to another, thus influencing the model estimation. More importantly, policy formulation requires activity data to comprehensively control pollutant reduction. Detailed and realistic classifications are conducive to policy analysis and fine-grained management, the detailed classifications of non-road machinery are thus needed to construct emission inventories.

In this study, Baoshan District, Shanghai is selected as a pilot area to establish a more detailed classification of non-road machinery that is more in line with port cities. Considering that local activity levels data, such as fuel consumption and working hours of non-road machinery, are not available in Shanghai, we obtained these data to correct the irrationality in the guidelines. More simple emission estimation methods are applied to estimate the emissions of PM₁₀, PM_2.5, HC, NO_x, CO, and SO₂ from non-road machinery in Baoshan District in 2021. Obtained from the questionnaire and field sampling, the activity levels are combined with the average emission factors to calculate. This approach avoids the loss of manpower resulting from statistics of all non-road machinery, and reduces the uncertainty by considering the effects of power and emission standard on machinery emission factors. In addition, a geographic information system (GIS) based on the population of machinery is applied to analyze the spatial distribution and sources of pollutants. Furthermore, the uncertainty of the estimated emissions is analyzed by the Monto Carlo simulation, and the pollutant volume discharged is predicted in future years by driving factors. Some relevant reduction measures are also discussed for the benefit of policymakers.

 
2 METHODOLOGY

 
2.1 Study Area

Baoshan District of Shanghai is selected as the target area. It is located at 31.26–31.51 degrees north latitude and 121.30–121.53 degrees east longitude, which is shown in Fig. 1. Baoshan District is in the north of Shanghai, at the intersection of Huangpu River and Yangtze River. The area is 17.5 kilometers long from east to west and about 23.08 kilometers wide from north to south, with a total area of 365.3 square kilometers. Due to the characteristics of geographical location, Baoshan District is a waterway hub in the north of Shanghai with a total shoreline length of 6,061.3 meters including three major port zones (Zhang Hua Bang, Jun Gong Road, and Luo Jing). In addition, the largest steel company in China, Bao Stell, is located in this area. Thus, there are many non-road machineries used by port and industrial enterprises in Baoshan District. Non-road machinery emissions have been recognized as a serious source of air pollution in previous studies. It is necessary to estimate the emission of air pollutants from non-road machinery and put forward effective mitigation measures.

Fig. 1. Location of Baoshan District on the map of China.

2.2 Study Objects and Classifications

The use and pollutant emissions of non-road machinery in Baoshan District, Shanghai was systematically investigated. The classification of non-road machinery is an important basis of emission inventories for further evaluation of sources. Since many previous non-road machinery emission inventory studies targeted inland locations, the classification of non-road machinery into agricultural and construction machinery was directly based on the classification recommended by the guidelines. However, as Shanghai is an important port city, the number of harbor machinery should not be underestimated and its activity levels would be quite different from those of construction machineries, so we refined the classification according to the actual machinery use. Therefore, non-road machinery in our work was classified into five parts, including in-plant machinery, agricultural machinery, construction machinery, harbor machinery, and other machinery. Moreover, due to the wide variety of machines, these machines are further categorized as different sub-categories based on different modes of operation. The details are listed in Table 1.

2.3 Estimation Methods of Pollutant Emissions

Three kinds of calculation methods were applied to estimate the pollutant emissions (SO₂, PM₁₀, PM_2.5, CO, HC, and NO_x) of non-road machinery in Baoshan District in 2021. They are presented in the following sub-sections.

 
2.3.1 Calculation of SO₂ emissions

A mass balance method was applied to measure SO₂ emission through combusting diesel, as shown in Eq. (1):

where E_i is the SO₂ annual emissions (t) of machinery i; P_i is the population of machineries i; M_SO2(64) and M_S(32) are the formula weight of SO₂ and S, respectively. Y_i is the fuel diesel consumption (t) of machinery i per annum, which is described in Section 2.3. S_i is the sulfur content of diesel (mg kg^–1) consumed by machinery i, we chose 50 mg kg^–1 according to the corresponding law of general diesel fuel in Shanghai. Considering that there can be a sulfur element that becomes part of the particulate matter in ionic form, it is necessary to deduct the sulfur content in the process of estimating SO₂ emissions using the sulfur content of diesel. E_i,PM10 is the PM₁₀ annual emission (t) of machinery i. C is the sulfur content of PM (%) consumed by non-road machinery, we used 0.017% according to the data provided by EPA's Speciate Database.

 
2.3.2 Calculation of pollutant emissions from agricultural machinery

In most cases, the engine power-based method was employed to calculate the emission of non-road machinery. However, the source of power for agricultural machinery cannot be limited to diesel, there are also gasoline, electricity, and other energy sources. Thus, the usage rate of fuel diesel is a correction factor to approximate the mechanical reality. The equation used in this study is given in Eq. (2):

where E_k is the annual emission (t) of pollutant k; A is the usage rate of fuel diesel in agricultural machinery (%), which was obtained from the China Agricultural Machinery Industry Yearbook (Agricultural Machinery Industry Yearbook). We assumed that A is 81.6% on basis of data provided by the 2020 Agricultural Machinery Industry Yearbook. LF_i is the load factor (0.65) of machinery i provided by Guidelines. G_i and hr_i are the average installed engine power (kW) and yearly working hours(h) of machinery i, respectively. G_i and hr_i are described in Section 2.3.1. EF_i,k is the emission factor (g kW h^–1) of pollutant k in machinery i.

 
2.3.3 Calculation of pollutant emissions of non-road machinery except agricultural machinery

As for in-plant machinery (except for in-plant forklifts), construction machinery, harbor machinery, and other machinery, these machineries are usually powered by diesel. The pollutant emissions of these machineries can be estimated by using the emission factor method of engine power. The equation used is shown in Eq. (3):

where P_i, G_i, LF_i, hr_i, EF_i,k are the same as defined for agricultural machinery. The diesel-power in-plant forklifts are replaced gradually by electric power in order to realize the goal of cleaner production. In the last decade, the proportion of sales of forklifts powered by diesel has dropped from 73.5% to 48.7%. Therefore, it is necessary to evaluate the usage rate of fuel diesel in in-plant forklifts. Eq. (2) can be used to estimate the emissions of in-plant forklifts, where A is the usage rate of fuel diesel in in-plant forklifts (%) obtained from the China Industrial Vehicle Industry Yearbook (Industrial Vehicle Industry Yearbook). We assumed that A is 59.5% based on data supplied by reports from 2011 to 2020 of the Industrial Vehicle Industry Yearbook.

 
2.4 Data Collection

2.4.1 Activity data

The sampling and questionnaire survey were conducted through field visits. The actual information of 555 non-road machineries was obtained, accounting for 12.4% of non-road machinery worked or registered in Baoshan District.

(1) Population

The population of the non-road machinery is important basic data to establish the emission inventory. In recent years, the declaration and registration system of non-road machinery in Shanghai has been gradually improved. Relevant information on numbers, types, owners, and owner locations of non-road machinery is provided by Ecological Environment Bureau in Baoshan District, Shanghai.

(2) Average installed engine power (AIEP)

In the previous studies (Van Linden and Herman, 2014; Wang et al., 2016; Lu et al., 2017; Zhang et al., 2017), the method of fuel diesel consumption has been applied to estimate the emissions of pollutants from machinery. The effects of the type, power, and load factor cannot be considered in this method which needs to be improved. Hence, an engine power-based approach, characterized by the average installed engine power (AIEP) of different subcategories of non-road machinery, was applied to estimate emission factors in this study. To obtain the AIEP, firstly populations and installed engine powers of each sub-category of non-road machineries were collected by sampling, and then the specific-machinery AIEP in the study area was calculated by dividing the summing power by its corresponding population. Each AIEP calculated through sampling was applied to each sub-category in totality (Table 2).

(3) Diesel consumption per annum

Diesel consumption and sulfur content were the most essential indices to calculate the emissions of SO₂. Sulfur content can be related to the quality of diesel. The sulfur content of diesel consumed by non-road machinery in Baoshan District is no more than 0.0050% according to the investigations. For the acquisition of annual diesel consumption data, there is no exact statistical material for non-road machinery in Baoshan District. Hence, diesel consumption per annum of machineries from different sub-categories was collected through questionnaire surveys, which are listed specifically in Table 2.

(4) Working hours per annum

Working hours have been considered as one of the indicators to estimate pollutant emission of non-road machinery. Working hours per annum of different types of non-road machinery provided by Guidelines compiled by the State Ministry of Ecology and Environment are generally lower than actual investigation results in many previous studies (Li et al., 2012; Lu et al., 2017; Bian et al., 2018; Fan et al., 2018; Xie et al., 2019). This results in the underestimation of emissions. For this reason, the data source was from questionnaire surveys and the recommended values in the Guidelines, as shown in Table 2.

 
2.4.2 Average emission factor (AEF)

The emission factors based on power were obtained from the recommended value in the Guidelines. Although the effect of emission stages and power segments on emission factors were taken into account in these Guidelines, it may be unpractical to obtain all information like these in the area. Therefore, AEF was used to meet the current situation and simplify the calculation. The equation is shown in Eq. (4):

where EF_i,k is AEF (g kW h^–1) for pollutant k of machinery i; EF_i,k,m,n is the primary emission factor (g kW h^–1) for pollutant k of machinery i, power segment m  and emission stage n; C_i,m,n is the proportion of the population (%) of machinery i under power segment m and emission stage. It is worth noting that C_i,m,n is calculated from sample data, as an estimated value of totality. Table 1 shows the results of AEF for each sub-category of non-road machinery in 2021 for the Baoshan District.

 
2.5 Estimation of Pollutant Emissions

Due to the inherent uncertainty in the estimation of pollutant emissions for the current year, the uncertainty in future pollutant emission predictions will be significantly increased, resulting in a loss of credibility in the prediction results. To address the question, the future scenario forecasting method was adopted and several specific scenarios were selected to reflect the differences in emission conditions under different scenarios. Since the populations of machineries, power, working hours, emission factors, and fuel diesel consumption could affect pollutant volumes discharged, the parameters that are sensitive to the results were selected as the main factors. Diesel consumption is the main driver for predicting the emission of SO₂. For the other pollutants excluding SO₂, the populations of machinery are the essential factor. Meanwhile, diesel consumption is closely related to the population of machineries. It is important to reasonably predict the changes in the population of non-road machineries. Three scenarios were set up to make the prediction more convincing. The equations for predicting volumes discharged in 2025 can be the same as those for estimation in 2021. Thus, except for population, none of the other parameters in the above equations are adjusted.

 
2.5.1 Prediction of populations

Some studies have found that the populations of agricultural machinery, in-plant machinery, construction machinery, and harbor machinery can be closely related to primary industry output, industrial output, building construction area, and port cargo throughput, respectively (Li, 2017; Huang et al., 2018). Therefore, the change rates of these economic activity indicators were used to predict the population of machinery in 2025. For other machinery, we used the growth rate of the tertiary industry output to make the predictions. Based on the gross regional domestic product (GRDP), industrial structures, and related economic activity indicators of Baoshan District, the annual rates of change of various forecast indicators were utilized to calculate the populations of various types of machinery in 2025. The predicted population of machines was obtained, leading to the further calculation of diesel consumption in 2025 to predict the emissions of SO₂ in 2025. It should be noted that no data on regional port cargo throughput are published in Baoshan District. There is a strong linear relationship between GDP per capita and port cargo throughput (Huang et al., 2018). Thus, the change in GDP per capita in Baoshan District can be put into use to predict the number of harbor machinery. To make the prediction more reasonable, three types of calibration factors, low (0.85), medium (1.00), and high (1.15) were set to reflect the low, normal, and high levels of economic development. The change rates of the numbers of different types of machinery we forecasted for 2025 are shown in Table S1. 

 
2.5.2 Spatial distribution methods

According to the location and population of non-road machinery, the location of pollution sources and annual emissions were determined, and the latitude and longitude and annual emissions were corresponded to each other. Then, these point sources were gridded using the spatial analysis function of ArcGIS software and assigned to a 200 × 200 m grid. Finally, the spatial distribution maps of pollutant emissions were obtained.

 
3 RESULTS AND DISCUSSION

 
3.1 Statistic Results

By using the data provided by the government sector, non-road machineries registered in Baoshan District were collected. The results show that the populations of in-plant machinery, agricultural machinery, construction machinery, harbor machinery, and other machinery are 3438, 280, 254, 378, and 112, respectively.

To estimate the pollutant volumes discharged, survey questionnaires were designed to collect basic information on non-road machineries. The type, population, installed engine power, emission standard, average daily working hours, working days, and total fuel consumption per annum were contented in the questionnaire. A total of 517 non-road machinery information was collected and specific statistical results are presented in Figs. S1–S3.

 
3.2 Current Pollutant Emissions from Machinery Sources

By adopting the methods described in Section 2, the pollutant emissions in Baoshan District were worked out. The results suggest that the total emissions of PM₁₀, PM_2.5, HC, NO_x, CO, and SO₂ are 138.40, 128.68, 425.77, 2000.51, 1762.27, and 1.31 t, respectively. Above all, NO_x and CO are major pollutants of non-road machinery emitted. Volume discharged of PM₁₀ is approximately 13.00% of the emissions of motor vehicles in Shanghai, indicating that non-road machineries have a significant impact on atmospheric PM₁₀ pollution. The study of Lang et al. (2017) has demonstrated that these pollutants can be important precursors of atmospheric PM_2.5 pollution and the general environmental pollution problem in most areas of China. Accordingly, pollutant emissions of non-road machinery cannot be overlooked. It is essential to formulate relevant measures, like on-road vehicles, to control these emissions.

 
3.2.1 Spatial distribution of machinery emissions

By using the method described in Section 2.5.2, the spatial distributions of emissions in the townships of Baoshan District are illustrated in Fig. 2. The maps of pollutant emissions (PM₁₀, PM_2.5, HC, NO_x, CO, and SO₂) represent similar spatial distributions. In general, the emissions from non-road machinery predominantly affect the eastern region of Luojing Town, the northern region of Yuepu Town, the eastern region of Yanghang Town, the western region of Youyi Road Street, and the southwestern region of Wusong Street, all of which have large emission sources (the red grids show extremely high emissions). Indeed, many harbor machineries are centralized in these areas, especially in the eastern part of Luojing town, where 70% of the harbor machineries are concentrated. The long working hour of these harbor machineries per day causes these areas to present such high emissions. For clear identification, the sources with high emissions in these emission spatial distribution maps were marked with red circles. Most notably, one factory in the eastern part of Luojing town was the largest source of pollutant emissions, which emited two to three times more pollutants than the other two high-emission point sources.

Fig. 2. Spatial distribution of pollutant emissions of non-road machinery in the Baoshan District.

Contrarily, low emission volumes are observed in most areas of Yuepu Town, Yanghang Town, Gucun Town, and Luodian Town, particularly in the southeastern part of Yuepu Town, the central part of Yanghang Town, the northwestern part of Gucun Town, and the southwestern part of Luodian Town where non-road machinery is not prevalent. Interestingly, there are many emission sources in the area between the southwestern part of Yuepu Town, the northwestern part of Yanghang Town, the northeastern part of Gucun Town and the southeastern part of Luodian Town, where approximately 23% of non-road machineries can be distributed and used according to our investigations. However, their emissions are not significant due to the use of forklifts, which constitute over 80% of the machines in the area. Forklifts have short annual operating hours and many are electrically powered, thus resulting in low emissions as shown by the dark green grids on the maps.

 
3.2.2 Sources analyses

The pollutant volumes discharged by different types of machinery in Baoshan District are shown in Fig. 3. The total emissions of in-plant machinery, agricultural machinery, construction machinery, harbor machinery, and other machinery are 2075.34, 40.85, 193.67, 2088.33, and 58.74 t, respectively. Since SO₂ emissions can be mainly related to the annual fuel consumption of the machinery and those from the in-plant machinery are the largest, this pollutant indicator will be discussed separately below. Except for SO₂, the total pollutant volumes discharged by in-plant machinery and harbor machinery are higher than other types, accounting for 46.54% and 46.85% of total non-road machinery emissions, respectively. Such results are closely related to the populations and working hours of machinery. It can be predicted that the high emission results for in-plant machinery since this type of machine accounts for 77% of the total surveyed non-road machinery. For harbor machinery with a small proportion, the special working nature of the port leads to higher annual working hours for this type of equipment.

 Fig. 3. The pollutant volumes discharged from different types of machinery in Baoshan District.

For SO₂, 73.276% of total emissions can be emitted by in-plant machinery. From the view of subtype, forklifts are to blame for the largest contribution to SO₂, consistent with the conclusion of the previous report (Guo et al., 2020). According to the survey, the proportion of forklifts mechanically fixed in the factory is 85%, basically including electric forklifts and diesel forklifts. Although the application of electric forklifts can be encouraged in the relevant policy (MEE, 2018), the price and operation and maintenance costs of diesel forklifts are much lower, so the number of diesel forklifts in use far exceeds that of electric forklifts. Widespread use of diesel-powered forklifts leads to high SO₂ emissions.

The maps of emission contributions by source are presented in Fig. 4. The pollutant volume discharged presents a decreasing trend from north to south and the volume emitted by Luojing Town is much higher than other townships. The area with the largest emissions from in-plant machinery is Luojing Town, followed by Yanghang Town and Luodian Town in order. For Miaohang Town, Zhangmiao Street, and Gaojing Town, the pollution in these areas can come from in-plant machinery, but the volumes emitted in these areas are low on account of the limitation of the quantity. The emissions from harbor machinery could be mainly distributed in Luojing Town, Songnan Town, and Wusong Street, which are close to the sea or the Huangpu River for convenient transportation of containers. For construction machinery, volumes discharged are concentrated in Youyi Road Street and Yuepu Town. The figures show that the volumes emitted by agricultural machinery are quite low in all townships, primarily concentrated in Luodian Town. From the point of view of industrial structure, the tertiary and secondary industries are dominated in Baoshan District. The proportion of primary industries decrease from 0.275% to 0.074% in 2012–2019, which results in a low number of agricultural machineries. These suggest that the emissions control of non-road machinery in Baoshan District should be focused on in-plant machinery and harbor machinery in the future.

Fig. 4. Emission contributions of each type of machineries in Baoshan District.

The least number of agricultural machineries has relatively high emission factors. This type of machinery normally lacks effective management, resulting in long-term use and aging. From the above results, for non-road machinery emissions, the control of pollutants from in-plant and harbor machinery is of great importance. In addition, the contribution of individual agricultural machinery is nonnegligible due to its high emissions.

 
3.2.3 Uncertainty analysis

Uncertainties were inevitable in the estimation of pollutant emissions from non-road machinery in this study. Hence, uncertainty analysis was conducive to formulating strategies for emission control and improving the data collection to reduce estimation-induced errors. Monte Carlo simulations were employed in the uncertainty analysis of estimations of air pollutant emissions. A Monte Carlo simulation could generate thousands of scenarios instead of just one in the probabilistic model approach. Some parameters, such as the mathematical distributions and the coefficients of variation (CV, the standard deviation divided by the mean) of the average emission factor, the average installed engine power, working hours per annum, population, and diesel consumption per annum, need to be confirmed before using Monte Carlo simulations. Considering the population data of non-road machinery provided by a relevant department, populations were assumed to be a normal distribution with a CV of 5% (Zhao et al., 2011; Lang et al., 2016). Average emission factors were defined as 30% of CV with a lognormal distribution (Wang et al., 2016; Zavala et al., 2017). Based on the assumption, the study defined 20% of CV with the normal distribution for diesel consumption (Karvosenoja et al., 2008). In addition, average installed engine power and annual working hours were considered to be the normal distribution with a CV of 10% in line with field surveys. Before the parameters were confirmed, 100000 simulations were conducted. Table S2 presents the results of these simulations, with 95% confidence intervals applied.

 
3.3 Prediction of Emissions

The emissions under different scenarios for Baoshan District in 2025 were predicted according to the method of driving factors. In this study, the prediction emissions of each type of machinery in Low, Medium, and High scenarios were shown in Fig. S4. The different economic development levels of Baoshan District are reflected in the three scenarios. The medium scenario means that every industry would be developed smoothly in the future in accordance with the 14^th Five-Year Plan. The total discharge of pollutants from in-plant machinery and agriculture machinery would drop by 12.72% and 4.89%, respectively. Because of the growth of the building area in Baoshan District in recent years, the total discharge of construction machinery can be predicted to rise by 6.01%. Besides, the total discharge would increase by 4.25% and 7.85% for harbor machinery and other machinery.

 
3.4 Emissions Estimates Based on Reduction Measures

Estimating pollutant emissions by using current emission standards and activity levels, the pollutant emission and diesel consumption would be estimated to maintain a high level. Therefore, it is particularly important to control pollutant emissions from the aspects of emission standard improvement, exhaust gas treatment, and oil product improvement. The emission standard can be upgraded to reduce the emissions of pollutants from the generation process. Hence, this study raised the emission standard of all machineries to National III, using Eq. (4) to derive new emission factors, as shown in Table 1. While the exhaust gas treatment equipment is an end-of-pipe treatment and oil product improvement is a measure to reduce the sulfur content from the source. The measures exclude the estimated emission reductions and all the emission reductions were estimated below based on treatment efficiencies from the literature.

(1) Oil product improvement

At present, the sulfur content of general diesel fuel used in Shanghai is no more than 50 ppm, which is the same as automobile diesel fuel that meets fourth-stage national standards. In order to further control the volume emitted of SO₂, the sulfur content of diesel fuel should be raised to a level comparable to automobile diesel fuel that meets fifth-stage national standards, with a sulfur content of no more than 10 ppm, which can reduce SO₂ emissions by 80%. The sulfur content requirement was the same as the oil standard in the EU’s Stage V Emission Standard for non-road mobile machinery. However, oil product improvement cannot work for other pollutants.

(2) Exhaust gas treatment

The field survey found that most of the non-road machinery used in Shanghai is lack exhaust gas treatment devices, without pollutant control. Gas treatment units equipped with non-road machinery are mainly elective catalytic reduction (SCR) and diesel particulate filter (DPF). For non-road machinery installed with DPF, the particulate emissions would be reduced by 80% or even 98% (Kang et al., 2018; Li et al., 2019). SCR has a significant effect on reducing the NO_x content of exhaust gases. Studies indicate that NO_x emissions would be reduced by 80% with the use of SCR alone (Desouza et al., 2020). The effects of NO_x treatment would reach 95% by combing SCR with DPF. At present, many of the harbor machineries in Baoshan District had installed DPF and SCR, and pollutant emissions get obvious control. Therefore, Baoshan District and even Shanghai could gradually promote the application of exhaust gas treatment equipment in non-road machinery through financial subsidies.

(3) Emission standard improvement

China's national emission standards for non-road machinery diesel engines have been in place since 2007. The current National Emission Standard III was implemented on October 1, 2014, while the National Emission Standards IV would be carried out in December 2022. According to investigations, many non-road machineries in Baoshan District that meet the first and second stages of national standards are still in use. The emission factors of these machineries are relatively higher than machineries that met third-stage national standards. If these machineries that meet the first and second stages are replaced with those that meet the third stage, average emission factors of different sub-categories of non-road machinery would decrease to varying degrees. The diminution of the average emission factors would lead to a reduction in the calculation of pollutant emissions, where PM₁₀, PM_2.5, HC, NO_x, and CO will decrease by 12.70%, 14.02%, 7.84%, 22.53%, and 6.51%, respectively. Thus, appropriate policies to phase out these old machineries need to be introduced and implemented to reduce pollutant emissions in Baoshan District. With the above three measures, SO₂, PM₁₀, PM_2.5, HC, NO_x, and CO can be estimated to be reduced by 80.00%, 82.54%, 82.80%, 7.84%, 84.51%, and 6.51%.

3.5 Comparison with Other Studies

The classification of non-road machinery and the proportions of each type of non-road machinery emissions to total pollutant volume discharged from previous studies during the past several years are summarized and compared with our study (Lu et al., 2017; Bian et al., 2018; Ye et al., 2018; Zhang et al., 2020; Liu et al., 2021). The results are shown in Fig. 5.

Fig. 5. Comparisons with previous studies.

From the perspective of agricultural machinery, the proportion of estimated agricultural machinery emissions to total pollutant volume discharged in this study is smaller than Lu et al. (2017) and Ye et al.’s (2018) results but is far less than the results of Zhang et al. (2019) and Bian et al. (2018). For construction machinery, the estimated proportion is smaller than the results of the previous studies. This result is due to the fact that this study only considered fixed construction machinery, in which the number is much smaller than that of mobile construction machinery. Due to the difficulty of determining the emission distributions, mobile construction machinery cannot be included in this study. For in-plant machinery, there are relatively large proportions of pollutant volume emitted from in-plant machinery in Shanghai (Lu et al., 2017), Guangzhou (Ye et al., 2018), and Shenzhen (Liu et al., 2021), similar to this study. From the angle of harbor machinery, the results of Lu et al. (2017) and Bian et al. (2018) have shown both Shanghai and Guangdong have significant emissions from harbor machinery, also similar to this study, while Shenzhen (Liu et al., 2021) has a relatively small percentage of harbor machinery emissions.

The above differences may be caused by the following reasons: (1) The classification type of non-road machinery is different. For example, non-road machinery is only classified into construction and agricultural machinery in Zhang et al.’s (2020) study. On this basis, Liu et al. (2021) and Bian et al. (2018) newly added harbor machinery and Liu et al. (2021) first appended in-plant machinery into the classification. In this study, non-road machinery is further classified into in-plant, agricultural, construction, harbor, and other machinery, which is conducive to meticulous environmental management. (2) Due to distinct leading industries in different regions, the machinery used varies greatly. For example, the agricultural machinery in Guangdong Province (Bian et al., 2018) is widely distributed and held in large quantities. While the value of the primary industry in Baoshan District, Shanghai in 2020 only accounted for 0.065% of GDP, which shows that the number of agricultural machineries in Baoshan District is extremely small. (3) Different calculation methods have an impact on the prediction results. Zhang et al. (2020) and Lu et al. (2017) used the calculated methods of fuel consumption, the engine power methods were applied by Liu et al. (2021) and Ye et al. (2018). While this work and Bian et al. (2018) constructed the localized comprehensive emission factors for emission estimation on the base of the engine power methods. (4) The activity levels are different in the various studies. According to the investigations, the working hours of the motor tractors used for port work in this study were 7905 h while Liu et al. (2021) used 770 h according to the recommended value in the guidelines.

In contrast to previous studies, this study corrected the parameters such as working hours recommended in the guidelines by questionnaire survey. In addition, a smaller spatial resolution was estimated to facilitate accurate industry management. Most importantly, in order to reduce uncertainty in emission inventories, this study established a more detailed classification of non-road machinery and calculated pollutant emissions using average engine installed power and average emission factors.

 
4 CONCLUSION

According to available information, the emissions inventory of non-road machinery in the Baoshan District was established in the study. The total emissions of PM₁₀, PM_2.5, HC, NO_x, CO, and SO₂ can be 138.40, 128.68, 425.77, 2000.51, 1762.29, and 1.31 t, respectively. Despite the low proportion of harbor machinery, the contribution to the source of pollution can be comparable to that of in-plant machinery, resulting in the highest emissions presented in Luojing Town. Except for SO₂, the total pollutant volumes discharged by in-plant machinery and harbor machinery were obviously higher than other types, accounting for 46.5% and 46.9% of the total emissions of all non-road machinery, respectively. For SO₂, 73.3% of the total volume was emitted by in-plant machinery and forklifts were to blame for this situation. In addition, the contribution of individual agricultural machinery was not negligible due to its high emissions.

The total volumes discharged of in-plant machinery and agricultural machinery in Baoshan District in the medium scenario were predicted to decrease by 12.72% and 4.89% in 2025. For construction machinery, harbor machinery, and other machinery, the total emissions predicted raised by 6.01%, 4.25%, and 7.85%, respectively. Moreover, some effective mitigation measures of non-road machinery were provided for reference in this study. By using the method of oil product improvement, exhaust gas treatment, and emission standard improvement, pollutant emissions can be substantially reduced.

According to the estimated data, the pollutant emissions from in-plant machinery and harbor machinery were greatly underestimated, especially a large amount of SO₂ and NO₂ from in-plant machinery and harbor machinery, which suggested that regulation is necessary. This also indicated that the activity data in the guidelines deviated from reality, demonstrating the necessity of this study. There are certain shortcomings in this study. Most of the emission factors used are from the guidelines and these factors may be affected by some factors such as non-road machinery manufacturers and models, so the next step is to establish localized emission factors to reduce uncertainty.

 
ACKNOWLEDGEMENT

We appreciate the anonymous reviewers for their valuable comments. This work was supported by the projects called Demonstration of high-resolution emission source spectrum construction and collaborative application in coastal areas (19DZ1205001) and Key Reserch and Development Projects of Shanghai Science and Technology Commission (20dz1204005).