Satellite Support to Estimate Livestock Ammonia Emissions: A Case Study in Hebei, China

Abstract

Ammonia (NH₃) is an important precursor of secondary inorganic aerosols that affect air quality and human health. Livestock production is an essential source of NH₃ emissions, which exceeded half of the total NH₃ emissions in China. However, our understanding of the livestock point NH₃ emissions is still limited, due to the lack of both monitoring and statistical data. In this study, we established a satellite-based approach to estimating livestock point NH₃ emissions by combining satellite observations and digital maps of points-of-interest (POI). Taking a case study in Hebei province over China, 1267 livestock points were identified. The point livestock NH₃ emissions in 2020 ranged from 16.8 to 126.6 kg N ha⁻¹ yr⁻¹, with an average emission of 42.0 kg N ha⁻¹ yr⁻¹. The livestock NH₃ emissions in Hebei showed an overall increasing trend, with a growth rate of 5.8% yr⁻¹ between 2008 and 2020. In terms of seasonal changes, high livestock NH₃ emissions mainly occurred in spring and summer, while low NH₃ emissions were generally in autumn and winter. Satellite-derived point livestock NH₃ emissions in Hebei were 2–4 times that of bottom-up NH₃ emissions (EDGAR), suggesting that current used bottom-up emissions underestimated point livestock NH₃ emissions. This study proposed a framework for the satellite-based estimation of livestock NH₃ emissions, which is of great significance for relevant N management and NH₃ emission reduction policy formulation.

1. Introduction

The impact of increasing ammonia (NH₃) emissions on the environment has received extensive attention [1,2]. As an essential alkaline gas and aerosol precursor in the atmosphere, NH₃ reacts with acidic substances (such as nitric acid and sulfuric acid vapor) to generate secondary inorganic aerosols, and then forms fine particles that are indirectly harmful to human health [3,4]. Studies have indicated that future NH₃ emission reduction is a cost-effective PM 2.5 control strategy, compared to further control of SO₂ and NO_x [5,6]. In addition, the deposition of excess nitrogen from NH₃ emissions into terrestrial and aquatic ecosystems through wet and dry deposition can cause biodiversity loss, which, in turn, causes soil acidification and the eutrophication of aquatic environments [7,8]. The impact of NH₃ on greenhouse gases cannot be ignored either. NH₃ is an indirect source of nitrous oxide [9], and its deposition also affects carbon dioxide sequestration [10,11].

Agricultural activities are the most crucial source of NH₃ emissions, most of which come from livestock manure [12]. Livestock production generally accounts for more than half of agricultural NH₃ emissions, and global livestock NH₃ emissions increased by 45% from 1980 to 2018 (22–32 Tg N yr⁻¹) [13]. China is a major global NH₃ emitter and livestock producer. NH₃ emissions from livestock manure accounted for 52% and 44% of China’s total emissions in 2012 and 2016, respectively [14,15]. To develop detailed policies to reduce NH₃ emissions and their potential impacts on climate, ecosystems, and human health, it is necessary to determine the spatial distribution of NH₃ emissions from livestock feedlots and analyze their changes and effects.

Estimated NH₃ emissions from livestock are usually based on bottom-up calculations with information on emission activities and emission factors [15,16]. However, relative to other pollutants, bottom-up estimates of NH₃ emissions have greater uncertainty, one major reason being that the availability of livestock emission factors is largely dependent on agricultural management and field measurements, but the information is often not widely available or representative [17]. Furthermore, agricultural NH₃ emission factors are highly variable and influenced by environmental conditions, which are often not adequately captured by bottom-up approaches [18]. Most of the NH₃ emissions from livestock production are obtained by these estimates, as they have relatively rough spatial resolution and poor temporal continuity, which usually lacks the consideration of point NH₃ sources, such as small feedlots [16,19].

The top-down approach based on remote sensing provides another reliable way to calculate NH₃ emissions, with the advantage of capturing spatiotemporal variability [20]. Satellite observation data have been widely used to identify NH₃ emission hotspots and calculate NH₃ emission fluxes. Using Infrared Atmospheric Sounding Interferometer (IASI) satellite observations, Van Damme et al. identified and quantified 248 NH₃ emission hotspots of agricultural and industrial point sources [21]. Clarisse et al. obtained a new global point source inventory of over 500 localized and classified NH₃ point sources with a wind speed-adjusted super-resolution resampling method [22]. Dammers et al. proposed a catalog of potential NH₃ emission sources obtained by analyzing global annual mean NH₃ concentrations, based on the cross-track infrared sounder (CrIS) and IASI satellite observations, combined with relevant emission inventory [23]. Chen et al. estimated NH₃ emissions in the USA, constrained with IASI-NH₃ measurements by the community multiscale air quality modeling system (CMAQ) adjoint model [24]. Marais et al. calculated NH₃ emissions in the UK using IASI and CrIS, combined with the feedback ratio between concentrations and emissions obtained from GEOS-chem [25]. Evangeliou et al. simulated NH₃ effective time based on the chemical transport model (CTM) and mass balance model to calculate global NH₃ emissions [26]. Luo et al. used IASI observations and the GEOS-Chem simulations to estimate global NH₃ emissions, which were 30 % higher than the previous bottom-up result [27]. The above studies demonstrated that NH₃ emissions from point sources, countries, and even the world can be estimated continuously in space and time, based on satellite observations, without relying on emission factors. For a more detailed and adequate investigation of livestock NH₃ emissions, it is necessary to identify specific feedlot emissions and impacts using top-down calculations.

Here, based on a web map search, we build a limited catalog location information of livestock and poultry feedlots in Hebei, China. We used the 2008–2020 IASI NH₃ dataset and a top-down approach to estimate the NH₃ emissions from these feedlots, and the hotspot feedlots were segmented according to their emissions. The spatial distribution of NH₃ emissions from some feedlots and emission changes on different time scales were analyzed. This satellite-based inventory of livestock point sources is important, as it is a complement to feedlot distribution data, and provides an additional reference for decision makers to understand livestock point sources and plan related NH₃ reduction policies.

2. Method and Data

2.1. IASI NH₃ Observations

The first global map of NH₃ spatial patterns was gained by a conventional retrieval approach applied to IASI spectral [28]. IASI is a passive remote-sensing spectrometer carried on board the polar sun-synchronous MetOp satellite, which detects the infrared radiation emitted from the surface and atmosphere operating at the nadir mode [29]. The instrument’s capabilities depend on the thermal contrast between the surface of Earth and atmosphere [30,31]. The IASI instrument is sensitive to NH₃ absorption features, majoring in a range from 800 to 1200 cm⁻¹ [32,33], which has twice the measurements of NH₃ at overpass times (AM and PM) of 09:30 and 21:30 mean local solar time (LST) in one day [34]. The observational swath width of IASI instruments reaches 2400 km. IASI has an elliptical pixel footprint of 12 × 12 km at nadir and outermost pixels of 20 × 39 km at marginal swath [35].

The IASI-NH₃ reanalysis dataset version 3.1 was used, with a temporal coverage of 2008–2020, to estimate NH₃ emissions from feedlots and analyze spatiotemporal trends. The product is from the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), which is based on the retrieval framework of an artificial neural network for IASI (ANNI), with a slight increase in measurement sensitivity [36]. The European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5) was used as input meteorological parametric data to correct the observations [35,37]. The related product validation studies conducted in Colorado (USA) and Hefei (China) found no major biases [38,39]. This product has been used to study NH₃ trends on a global and national scale [40]. In this study, we only used the IASI daytime observations (AM) because the instrument is more sensitive to the high thermal contrast during the day [41]. The NH₃ columns were sampled at a resolution of 0.01°, and the daily NH₃ columns were interpolated to maintain the spatial continuity (Figure S1).

2.2. Livestock NH₃ Emissions

In this study, the semi-automatic method of POI search on the digital map (Amap/Gaode) is used to obtain the points of livestock feedlots. This relatively fast method can get relatively new livestock information. First of all, we used the keywords of “livestock”, “pasture”, or “pig, horse, cattle, sheep, chicken, duck” to search the potential livestock locations in Hebei. Then, we deleted those points that contained those keywords but were not livestock feedlots (Figure S2), based on the European Space Agency (ESA) global land use data at 10 m resolution in 2020 (Figure S3) and Google images. We obtained a total of 1267 livestock points in Hebei.

We estimated livestock NH₃ emission flux (E) by assuming a box model with the first-order loss [21]:

$$\mathrm{E}=\mathrm{M}/\mathsf{\tau }$$

(1)

where $\mathrm{M}$ is the total quantity of NH₃ supposed to be contained in the gridded box in kg, and $\mathsf{\tau }$ is the NH₃ effective lifetime in each gridded box in hours. We calculated NH₃ emission fluxes with the monthly average IASI-NH₃ columns, expressed as kg N ha⁻¹ yr⁻¹.

The effective lifetime of NH₃ is impacted by chemical loss and deposition significantly. The lifetime ($\mathsf{\tau }$) within each grid-box is affected by the three processes and defined for the model as:

$$\frac{1}{\mathrm{t}}=\frac{1}{{\mathrm{t}}_{\mathrm{trans}}}+\frac{1}{{\mathrm{t}}_{\mathrm{chem}}}+\frac{1}{{\mathrm{t}}_{\mathrm{dep}}}$$

(2)

The lifetimes associate with transport out of the grid-box (${\mathrm{t}}_{\mathrm{trans}}$), chemical loss (${\mathrm{t}}_{\mathrm{chem}}$), and deposition (${\mathrm{t}}_{\mathrm{dep}}$).

The lifetimes of NH₃ have been primarily reported in a few hours to a few days (Table S1) [23,42,43,44]. Van Damme et al. assumed the global average value (12 h) as the NH₃ lifetime to estimate the emissions over 240 hotspots and regional sources [21]. Evangeliou et al. calculated the NH₃ lifetime using CTM and mass balance model, which found the global NH₃ average lifetime is 11.6 ± 0.6 h, with the difference between the highest and lower values at 16 h and 10 h [26]. This range was consistent with most previous studies, except for the point source lifetime of less than 4 h [23]. Luo et al. further estimated the survival time and found that the average effective time in the North China Plain (NCP) was within 5–15 h by GEOS-Chem [27]. Considering the uncertainty of NH₃ effective time, the emission estimate was performed given lifetime ranges (2, 12, and 24 h).

It is challenging to accurately estimate NH₃ emissions from feedlots through top-down methods. The main reason is that the emissions from mixed sources cannot be accurately distinguished, especially for feedlots in farmland areas. In this paper, we assume that all emissions from the feedlots are only within the grid where they are located, ignoring emissions from non-agricultural sources in the grid and the transfer between grids. Therefore, the estimated results of the emissions mainly depend on the NH₃ lifetime and concentrations. In addition, the calculation of NH₃ emissions from feedlots considers the regional characteristics of the feedlots (the proportion of built-up area and farmland in the grid where they are located) (Figure S4). For those feedlot grids with a significant background crop area and small feedlot size, the emissions were defined as half of the total calculated emissions (Figure S5).

3. Results

3.1. Spatial Distribution of Identified Livestock Locations

Figure 1 shows the three-year average NH₃ columns from 2018 to 2020, the spatial distribution of animal husbandry feedlot locations, and corresponding estimated NH₃ emissions in 2020 over Hebei province, China. The range of NH₃ concentrations in animal husbandry feedlot locations of Hebei province in three years is 6.3–64.8 × 10¹⁵ molec·cm⁻², with an average of 31.3 × 10¹⁵ molec·cm⁻². NH₃ concentration in Hebei province is generally high in the southeast and low in the northwest. The high NH₃ concentration is mainly concentrated in the plain (part of the NCP) with intensive agricultural production.

The 1267 animal husbandry feedlots in Hebei province were identified with point sources, most of which are concentrated in the south and east of Hebei province. Xingtang county (in Shijiazhuang city) had the largest number of animal husbandry feedlots, with 38 feedlots located there, followed by Cang county (in Cangzhou city), with 28 livestock feedlots. The estimated average NH₃ emission of 1267 livestock feedlots in 2020 is 42.0 kg N ha⁻¹ yr⁻¹. Among them, the hotspot feedlots with emissions above 100 kg N ha⁻¹ yr⁻¹ accounted for 0.8% of the inventory, and half of the feedlots were above 40 kg N ha⁻¹ yr⁻¹. Minfu (in Baoding city) has the largest emission, with a value of 126.6 kg N ha⁻¹ yr⁻¹, while Jindong (in Zhangjiakou city) has the lowest emission, with an annual emission of only 16.8 kg N ha⁻¹ yr⁻¹.

3.2. Livestock Point Sources

Figure 2 shows the satellite images of the six livestock feedlots and distribution of the average NH₃ concentrations (2018–2020) in the corresponding areas. The list of feedlots in this study mainly includes intensive and family livestock feedlots. Family livestock feedlots are generally distributed in villages and towns, with a small scale but a large number, while intensive feedlots are generally large in scale. Most of the feedlots on the list are located on the fringes and suburbs of townships, close to roads and farmland (Figure S6). Among them, the large-scale intensive livestock feedlots have more distinct independent characteristics in the image, including large baskets and blue, red, white and brown sheds (such as Gaoxin in Figure S6b and Baogang in Figure S6d). Some incomplete closed intensive livestock feedlots also have relevant open garbage pits and livestock activity areas (such as Heshunnainiu and Minda in Figure S6a). On the other hand, most of the small-scale family feedlots are shown as village houses in the images (such as Shunxing in Figure S6a and Qiteng in Figure S6b), which generally breed poultry, such as chickens and ducks.

Satellite images of the six feedlots reveal that Jiaheshun (in Baoding city, Figure 2d) has relatively open-air feedlots, while Laozhang (in Zhangjiakou city, Figure 2a), Rundamuye (in Langfang city, Figure 2b), Fengfa (in Hengshui city, Figure 2c), Jinmu (in Zhangjiakou city, Figure 2e), and Xiaohui (in Hengshui city, Figure 2f) livestock feedlots are completely closed indoor livestock feedlots. Laozhang is a pig feedlot, which is located in the villages and towns in the mountainous flat area and has the lowest NH₃ column (10.0 × 10¹⁵ molec·cm⁻²). Fengfa (37.2 × 10¹⁵ molec·cm⁻²) is a dairy feedlot located on both sides of the canal. The column of Jinmu is high (47.5 × 10¹⁵ molec·cm⁻²) and located in the farmland with a large garbage pit.

Figure 3 shows the temporal and spatial changes of NH₃ concentrations in the six feedlots. The annual change of livestock NH₃ concentrations obtained by IASI is consistent with the establishment and expansion of the feedlots. When the livestock feedlot is not established, the NH₃ concentration is low, such as in the Laozhang, Fengfa, and Xiaohui feedlots. With expansion and reconstruction of the feedlots, the corresponding NH₃ concentrations in the livestock feedlots increase, which indicates that IASI can obtain the spatiotemporal trend of point livestock NH₃ concentrations on a small scale. Jiaheshun and Rundamuye have undergone minor expansions and renovations, but their NH₃ concentrations also increased rapidly, indicating that changes in NH₃ concentrations caused by other factors should also be considered.

3.3. Annual and Seasonal NH₃ Emissions of Livestocks

The examples in Figure 4 show the inter-annual and monthly changes in NH₃ emissions from six feedlots. The NH₃ emissions of these feedlots showed an overall growth trend during the past thirteen years. The emissions of Wangsen and Disan in Baoding city increased by 132.0% and 98.3% during 2008–2020, respectively. The annual average growth rates of the emissions from the two livestock feedlots were 1.85 kg and 1.35 kg N ha⁻¹ yr⁻¹ before 2015; after that, the annual average growth rates reached 13.1 and 8.7 kg N ha⁻¹ yr⁻¹. They reached the maximum emissions in 2019, which were 117.0 and 95.0 kg N ha⁻¹ yr⁻¹. Except for the two fast-growing breeding sites, NH₃ emissions of Fugui and Guangyi have also increased substantially, with their emissions increasing by 83.6% and 78.4%. Aizeng and Laiyuan also increased by 43.6% and 28.3% during this period. Based on the average NH₃ emissions of the livestock farms during 2018–2020, we divided the feedlots into 10 groups, with 10 kg N ha⁻¹ yr⁻¹ as the interval, to calculate the annual and monthly emissions of them, and found that the feedlots in Hebei province, as a whole, showed a growth trend, with an average growth rate of 5.8% yr⁻¹ over the past thirteen years (Figure S7).

In terms of the seasonality of emissions (Figure 4b), NH₃ emissions of these livestock feedlots generally peak in summer. For example, Wangsen reached its peak in July (16.8 kg N ha⁻¹ month⁻¹), and the Disan, Laiyuan, and Aizeng feedlots reached their peak in August (15.1 kg N ha⁻¹ month⁻¹, 11.1 kg N ha⁻¹ month⁻¹, and 6.9 kg N ha⁻¹ month⁻¹). However, Guangyi and Fugui reached emission peak (10.8 kg N ha⁻¹ month⁻¹ and 3.8 kg N ha⁻¹ month⁻¹) in spring (May). NH₃ emissions of these livestock feedlots were relatively low in autumn and winter, and the results of grouping calculations demonstrate that livestock feedlots with higher NH₃ emissions have similar seasonal patterns (Figure S8). This may be due to the higher temperature in the warmer months in Hebei province promoting NH₃ emissions, while the related breeding activities were frequent. It should be noted that the background emissions from cropland may affect the IASI observed seasonal characteristics of feedlots emissions.

3.4. Comparisons with Bottom-Up NH₃ Emissions

We compared IASI-derived NH₃ emissions of all breeding points with the bottom-up emission inventory, Emissions Database for Global Atmospheric Research v6.1 (EDGAR), at the county level in Hebei province (average NH₃ emissions from feedlots by county). As shown in Figure 5, 35% of the IASI-derived NH₃ emissions were within two times of EDGAR NH₃ emissions from manure, while 27.8% of IASI-derived NH₃ emissions exceeded four times of EDGAR. The comparison of them during 2008–2018 showed that R² was less than 0.3, and RMSE ranged from 13.2 to 23.5 kg N ha⁻¹ yr⁻¹ (Figure S9). The largest difference occurred in Kuancheng of Chengde city and Guyuan of Zhangjiakou city (more than 16 times), and the differences were 30.4 kg N ha⁻¹ yr⁻¹ and 9.9 kg N ha⁻¹ yr⁻¹, respectively. Compared to IASI-derived NH₃ emission estimates, EDGAR has limited resolution and tends to underestimate emissions at small scales, because it only represents a broader range of NH₃ emissions and cannot capture local details.

4. Limitation and Outlook

The livestock locations were obtained through the POI search of the Gaode map. Keywords were used to obtain livestock-related information, which may not be comprehensive; they may be redundant to some extent, and they were screened manually. The POI data of the livestock map captured was the record data of the latest year (2022), and the historical data was difficult to obtain. Because the data records of the network POI map are often uploaded artificially, there will inevitably be omissions, especially for the newly built breeding feedlots and isolated breeding areas. In addition, to ensure the automation of the process as much as possible, we have adopted stricter standards to screen the potential error livestock points, based on the land use map of ESA WorldCover at 10 m resolution.

In terms of the calculation of emission flux, this paper calculates the NH₃ emissions of the feedlots through the top-down method (first-order atmospheric box model). The model assumes that the atmospheric transport between grids is negligible and deposition or chemical reaction process is speedy. It is difficult to quantify the lifetime of NH₃ because it is affected by atmospheric transport, atmospheric deposition, and chemical reaction. Although the atmospheric model has been used to simulate the lifetime of NH₃, it is still limited by the low spatial resolution of the model. This study only used a fixed NH₃ lifetime from Van Damme et al., with uncertainty ranges reported, which may bring some uncertainty [21].

In addition to the calculation model and NH₃ lifetime, the uncertainty of the IASI measurements can contribute to the uncertainty of the NH₃ emissions. Previous studies have shown that the IASI measurement is more likely to underestimate the NH₃ columns than overestimate them [21], and the columns may have an uncertainty of 25%–50% [37,45].

Nevertheless, the point data of livestock feedlots in Hebei province and the top-down calculation method of livestock NH₃ emissions obtained here can provide an independent way to quickly obtain the basic information on livestock NH₃ emissions in a long time series. The data retrieval and NH₃ emission calculation methods proposed in this paper can also be further used for the data acquisition and estimating livestock NH₃ emissions throughout China and other regions.

5. Conclusions

We use satellite measurements from IASI to calculate and analyze the point livestock NH₃ emissions by combining the digital maps (Gaode). In 2020, livestock average NH₃ emissions in Hebei were 42.0 kg N ha⁻¹ yr⁻¹, and 0.8% of the feedlots in the list are above 100 kg N ha⁻¹ yr⁻¹. The NH₃ emissions from the six selected feedlots showed an overall increasing trend from 2008 to 2020; they remained stable from 2008 to 2015 and increased rapidly after 2015. The NH₃ emission of feedlots generally peaks in spring and summer, and the months with lower NH₃ emissions often appear in autumn and winter. The satellite imagery and IASI NH₃ dataset show that the IASI measurements can indicate the link between changes in feedlot size and NH₃ columns, to a certain extent, and the expansion of large-scale feedlots generally leads to the growth of NH₃ concentrations in the region. We compared the average emissions of feedlots from satellite and EDGAR on a county-by-county basis. In 35% of counties and districts, the difference in average emissions between them is less than two times. The EDGAR underestimates NH₃ emissions from feedlots, compared with satellite-based calculations. This article provides a new feedlot data source and perspective for feedlot NH₃ emission calculation, which is an important reference for feedlot NH₃ emission control and its related policy formulation.