Quantifying the Influence of a Burn Event on Ammonia Concentrations Using a Machine-Learning Technique

Abstract

Although combustion is considered a common source of ammonia (NH₃) in the atmosphere, field measurements quantifying such emissions of NH₃ are still lacking. In this study, online measurements of NH₃ were performed by a cavity ring-down spectrometer, in the cold season at a rural site in Xianghe on the North China Plain. We found that the NH₃ concentrations were mostly below 65 ppb during the study period. However, from 18 to 21 November 2017, a close burn event (~100 m) increased the NH₃ concentrations to 145.6 ± 139.9 ppb. Using a machine-learning technique, we quantified that this burn event caused a significant increase in NH₃ concentrations by 411%, compared with the scenario without the burn event. In addition, the ratio of ∆NH₃/∆CO during the burn period was 0.016, which fell in the range of biomass burning. Future investigations are needed to evaluate the impacts of the NH₃ combustion sources on air quality, ecosystems, and climate in the context of increasing burn events worldwide.

1. Introduction

As an important alkaline gas in the atmosphere, atmospheric ammonia (NH₃) has a crucial influence on atmospheric chemistry and the nitrogen cycle [1,2]. It can react with sulfuric acid (H₂SO₄) and nitric acid (HNO₃), and enhance the formation of secondary inorganic aerosols (SIAs) [3]. Additionally, NH₃ can enhance the yield of secondary organic aerosols (SOAs) through aqueous chemistry [4]. These particulate SIAs and SOAs in the air decrease visibility, damage human health, and affect the climate [5,6,7,8,9]. After deposition, NH₃ can directly or indirectly affect terrestrial and aquatic ecosystems, such as soil acidification, water eutrophication, and reduction in biodiversity [10,11,12]. Thus, identifying and quantifying the sources of NH₃ is essential to understanding its vital role in atmospheric chemistry and reducing its negative impacts on the ecosystem and climate.

Although livestock waste and nitrogen fertilization are considered the most important sources of NH₃ emissions on a global or regional scale [13,14], NH₃ is also emitted into the atmosphere during the fuel combustion process through pyrolysis [15]. Biomass burning, such as forest fires, plays a critical role in NH₃ emissions in rural areas. For example, 1.4–8.2 and 0.7–2.6 Tg of NH₃ were emitted from forest fires in Indonesia in autumn in 2015, and Russia in July–August 2010, respectively [16,17], and 0.12 Mt of NH₃ was emitted from agricultural crop residue burning in India from 2008 to 2009 [18]. In urban areas, the nitrogen isotopic approach indicates that NH₃ originated primarily from combustion sources, including coal combustion, NH₃ slip from power plants, and vehicle exhausts [19,20,21]. A high-resolution global inventory revealed that NH₃ emissions from combustion sources continued to increase from 1960 to 2013 [22]. In China, an average of 8182 forest fires occurred from 1987 to 2007 [23]. The elevated fire frequencies were partially due to climate change [24]. Thus, the impact of combustion sources on NH₃ concentrations and related consequences needs further attention.

China is a global hotspot of atmospheric NH₃ emissions, with an annual increasing rate of 1.9% [25]. Notably, the North China Plain (NCP) is confirmed to be the largest region with high surface concentrations, and the highest emissions in China [26]. Despite the pollution reduction actions implemented since 2013, severe haze pollution events still occur in the cold season in this region. For example, the highest PM_2.5 concentration reached approximately 250 μg/m³ in winter in 2020 [27], and it was dominated by sulfate–nitrate–ammonium (SNA), especially nitrate aerosols [28]. Recent studies have suggested that NH₃ plays an important role in determining nitrate concentrations [29,30]. Therefore, it is necessary to observe the concentrations of NH₃ and investigate its emission sources in this region.

In this study, we performed online measurements of NH₃ in the cold season in Xianghe, a rural site in NCP. During the observation campaign, we detected an unexpected burn event that had a significant influence on the NH₃ concentrations. Finally, we attempted to combine a novel machine-learning technique based on the random forest (RF) algorithm, to quantify the impact of burn events. Such an understanding could be beneficial for controlling NH₃ emissions and further improving air quality in the future.

2. Data and Methods

2.1. Site Description

Since 2017, online measurements of NH₃ concentrations have been performed in Xianghe, NCP (39.75° N, 116.96° E) (Figure 1). The site is surrounded by residential areas that lack tall buildings and obvious industrial emission sources. Based on its location in the northern part of the NCP between Beijing (~45 km) and Tianjin (~70 km), the instruments at the site can detect pollutants of urban, rural, background or mixed origins, reflecting the complex changes in NH₃ in the NCP.

2.2. Data Sources

2.2.1. Measurements of NH₃

Hourly concentrations of NH₃ were measured at a high temporal resolution of 1 Hz, online, using a standard cavity ring-down spectrometer (CRDS) (G2103, Picarro Inc., Santa Clara, CA, USA). CRDS is a direct absorption technique that uses pulsed or continuous light sources and has a significantly higher sensitivity than conventional spectrometers [31]. In CRDS, two high reflectivity mirrors are used in the optical cavity to increase the absorption optical path length, thereby enhancing the contrast of the absorption signal of NH₃. The CRDS setting measures how long it takes for the light to drop to a certain percentage of its original intensity, and the “ring-down time” is used to calculate the concentration of NH₃ in the cavity [32].

To prevent water vapor from affecting the NH₃ spectrum, the manufacturer has incorporated a correction procedure for the reported NH₃ values [33]. In addition, to reduce the adsorption of NH₃, the Teflon tubing was insulated and warmed with heating tape (~45.7 °C). Meanwhile, a filter was installed at the front of the inlet to induce ambient air flow. According to the air conditions, the filter was replaced every 2 weeks to 1 month. The instrument was placed in an air-conditioned cabin laboratory; more detailed descriptions are documented elsewhere [34].

2.2.2. Other Supporting Data

Air pollutants and meteorological data are also used in this study. The hourly concentrations of SO₂, NO_x, CO, and PM_2.5 were measured at the same height as NH₃. Meteorological parameters, including temperature (T), relative humidity (RH), wind speed (WS), and wind direction (WD), were obtained from the China Meteorological Administration (http://data.cma.cn (accessed on 14 October 2021)).

2.2.3. Burn Event

During the observation campaign, a burn event occurred from 18 to 21 November, 2017. This burn event was caused by the combustion of garbage in the nearby residential area, which was approximately 100 m away from the observation site. The burned material was complex and included discarded paper, kitchen waste, crop residues, weeds, branches, and leaves.

2.3. RF Models

The temporal variations in NH₃ in this study demonstrated a cycle ranging from 4 to 7 days. These strong cycles are regional in nature and controlled by the passage of cold fronts [35]. This unique temporal feature indicated that the results presented are reproducible on a regular basis, and applicable for the RF model in predicting NH₃ dynamics with meteorological conditions.

In this study, we used a machine-learning technique to quantify the influence of burn events on NH₃ concentrations. First, we established a model (RF1) with observed NH₃ concentrations as the dependent variable, and predictors (meteorological parameters, time predictors, and regional transport parameters) as the independent variables (Table 1). RF1 was trained on datasets during the nonburning period (8–17 and 22–30 November 2017). The training set accounted for 80% of data, and the testing set included the remaining 20%. On the basis of RF1, a series of models (RF2–RF11) were established, according to the relative importance of the predictor variables for eliminating or adding some predictors (Table S1). A detailed evaluation of all the established models is provided in Table S2, including the coefficients of determination (R²), the fraction of predictions within a factor of 2 (FAC2), mean bias, normalized mean bias, root-mean-square error (RMSE), and Pearson correlation coefficient (PCC). The performance of RF5 was considered the best due to its higher R² and lower RMSE.

The RF models were developed using the rmweather R package. However, unlike previous studies, we further adjusted two important parameters of RF5, namely, the number of trees (ntree) and the number of variables split in each node (mtry). Mtry values from 1 to 8 with an interval of 1, and ntree values from 50 to 500 with an interval of 50, were selected. As shown in Figure S1, when ntree was 300 and mtry was 5, the simulation effect of the model was best, as indicated by the higher R² value and lower prediction error (MSE).

After the RF model was adjusted and optimized, the testing datasets were randomly selected to assess the correlation between the observed and predicted concentrations (Figure S2), to ensure that this model could make better predictions. Finally, we used this model to predict NH₃ concentrations under the assumption of no burn events. To evaluate the impact of the burn events (18–21 November 2017) on NH₃ concentrations, relative changes (R) between the observed concentrations (C_observed) and predicted concentrations (C_predicted) for NH₃ were defined using the following equation:

(1)$R=\frac{C_{observed}-C_{predicted}}{C_{predicted}}\times 100\%$

3. Results and Discussion

3.1. Changes in Observed Concentrations of NH₃

Hourly concentrations of NH₃ were selected from 8 to 30 November 2017 in this study, and their temporal variations are shown in Figure 2. We found that the NH₃ concentrations were mostly below 65 ppb during the study period, with the exception of 18–21 November, when a burn event occurred. Therefore, in the following sections, we have separated the dataset into two periods, i.e., the burning period (18–21 November 2017) and the nonburning period (8–17 and 22–30 November 2017).

During the nonburning period, the NH₃ concentrations in Xianghe ranged from 1.9 to 154.3 ppb, with a mean value of 25.4 ± 16.9 ppb. The NH₃ concentrations in this study were comparable to the urban observations (28.5 ± 11.6 ppb) in autumn in Beijing [36]. Moreover, the average NH₃ level measured in Xianghe was generally similar to observations in India (24.6 ± 5.0 ppb) [37], but much higher than those in Europe (1.2 ppb) and the United States (2.4 ppb) in autumn [10,38].

After the start of the burn event, the observed concentrations of NH₃ significantly increased, with the highest value exceeding 600 ppb. In addition, three concentration spikes occurred: at 7:00, 18 November (281.6 ppb); 2:00, 19 November (547.1 ppb); and 10:00, 20 November (601.4 ppb). During the burning period, the average NH₃ concentration was 145.6 ± 139.9 ppb, which is five times that in a previous report (26.6 ± 13.9 ppb) at the Xianghe site in the cold season [39].

In addition to NH₃, the concentrations of other air pollutants also significantly increased during the burning period, e.g., by 209.7%, 84.3%, 69.1%, and 79.8% for PM_2.5, SO₂, NO_x, and CO, respectively (Figure S3). These air pollutants had a positive correlation with NH₃ during the burning period, especially CO (R² = 0.74, p < 0.01) and NO_x (R² = 0.72, p < 0.01). However, their positive correlation was not significant in the nonburning period (Table S3). These results all indicated the potential influence of combustion sources on air quality. In addition, the intensity of combustion was closely related to weather conditions [40]. In our study, the NH₃ concentrations were higher during the burning period when the air mass originated from the northwest, and had a higher RH (56.2 ± 26.3%) and lower T (−1.1 ± 5 °C) and WS (1.6 ± 1.5 m s⁻¹) (Figure S4). To quantify the impact of the burn events on NH₃, we predicted NH₃ concentrations without burn events using a machine-learning technique in the next section.

3.2. Changes in Predicted Concentrations of NH₃

We first established a model (RF5) to predict the NH₃ concentrations assuming that no burn event occurred from 18 to 21 November 2017. The predicted results are shown in Figure 2, with all predicted concentrations lower than 65 ppb. In addition, the temporal variation in predicted NH₃ concentrations was basically similar to its observed value in the nonburning period. This finding indicates that the concentrations and temporal pattern of NH₃ will not change significantly if there is no burn event.

The difference between the predicted and observed concentrations of NH₃ allows us to quantify the impacts of burn events on NH₃ emissions. As shown in Figure 3 and Table S4, remarkable differences were not found between the observed and predicted concentrations of NH₃ before the burn event, indicating that our model captured the variations in NH₃ concentrations well. However, the observed concentration of NH₃ increased drastically compared with the predicted value during the burning period, with increased ratios of 336.9%, 561.1%, and 652.2% in the first three days of the burn event. At the end of the burning period and with the resumption of “normal emissions”, the predicted values were consistent with the observations after 22 November. Overall, the observed and predicted concentrations of NH₃ during the burning period were 145.6 ± 139.9 and 28.5 ± 10.4 ppb, respectively, resulting in an increase of 411%. These results indicated that the combustion source is an important factor of NH₃ emissions.

3.3. Dominant Source of NH₃ during the Burn Event

Although the overall impacts of the burn event on NH₃ concentrations were identified in the above sections, the potential dominant source contributing to the substantial increase in NH₃ was still unclear. To address this concern, we further investigated the relationship between NH₃ and other air pollutants.

As shown in Figure 4, we found that the ratio of NH₃ to other air pollutants significantly increased after the burn event occurred. Compared with those before burn events, the ratio of NH₃/PM_2.5, NH₃/SO₂, NH₃/NO_x, and NH₃/CO increased by a factor of 1.6, 2.5, 3.0, and 5.4 during the burning period, respectively (Table S5). This finding indicated a dramatic change of CO during the burn event. Thus, CO was selected as a tracer of fire [41]. Then, the emission ratio (ER) was defined as ∆NH₃/∆CO in our study [17,42], where ∆NH₃ and ∆CO represented the difference in the corresponding concentrations of NH₃ and CO before and during the burn event. In this study, the calculated ∆NH₃/∆CO value was 0.016, which was consistent with the characteristics of biomass burning (Table 2). Therefore, the dominant source emitting substantial NH₃ might be biomass burning in this burn event. Future control measures on NH₃ emissions should pay more attention to potential contributions from biomass burning.

4. Conclusions

In this study, the NH₃ concentrations were measured by the CRDS technique in the cold season at a rural site in the NCP. The hourly NH₃ concentrations were mostly below 65 ppb during the study period. However, an unexpected burn event caused a significant increase in NH₃ concentration from 18 to 21 November 2017, with peak values exceeding 600 ppb. With the aid of a machine-learning technique, we found that the burn event could cause a 411% increase in the NH₃ concentration. Notably, the ∆NH₃/∆CO ratio was 0.016 during the burning period, indicating that biomass burning might be the dominant emission source. Due to the increasing occurrence of burn events worldwide, the impacts of the combustion sources of NH₃ on air quality, ecosystems, and climate need to be further explored.

Author Contributions

Conceptualization, Y.P.; methodology, J.H.; software, J.H., Y.W. and Y.H.; validation, J.H.; formal analysis, J.H.; investigation, J.H. and Y.L.; resources, Y.P., D.J. and T.L.; data curation, J.H.; writing—original draft preparation, J.H.; writing—review and editing, Y.P. and W.S.; visualization, J.H.; supervision, Y.P. and W.S.; project administration, Y.P. and X.Y.; funding acquisition, Y.P., T.L. and X.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China [grant number 42077204] and the Open Research Fund Program of Plateau Atmosphere and Environment Key Laboratory of Sichuan Province (Project PAEKL-2020-C3).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on reasonable request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. Location of the NH3 observation site in Xianghe.

Figure 2. Temporal variation in hourly concentrations of NH3. The gray line indicates an NH3 concentration of 65 ppb. The blue shaded area represents the burning period, and the non-shaded area represents the nonburning period.

Figure 3. Observed and predicted concentrations of NH3 before, during and after the burning period at the Xianghe site. Percentages on bars indicate relative changes (R) between the observed and predicted concentrations.

Figure 4. Temporal variation in the ratio of NH3 relative to NOX, SO2 (a), CO and PM2.5 (b).

Table 1

All possible predictors for the RF models in this study.

Codes	Prediction Variables	Units
Meteorological parameters
T	Air Temperature	°C
WS	Wind speed	m/s
WD	Wind direction	degree
Pressure	Atmospheric pressure	hPa
RH	Relative humidity	%
Time parameters
day_Julian	Date of the year (1–366)	n/a
weekday	Day of the week (1–7)	n/a
hour	Hour of the day (0–23)	n/a
Air pollutants
PM2.5	Particulate matter	μg/m3
NOx	Nitrogen oxides	μg/m3
NO2	Nitrogen dioxide	μg/m3
SO2	Sulfur dioxide	μg/m3
CO	Carbon monoxide	ppb
NO	Nitrogen monoxide	μg/m3
Regional transport parameter
cluster	Back trajectory cluster	n/a

Table 2

Summary of the NH₃ concentration and its emission ratio (ER = ∆NH₃/∆CO) in different burn events.

Focus Region	Event	NH3 (ppb)		ER (ppb/ppb)	Reference
		Range	Average
Xianghe, China	Biomass burning	2.4–601.4	145.6 ± 139.9	0.016	this study
Shenyang, China	Vehicular exhaust	61.8–248.3	152.9 ± 55.6	–	[43]
Canada and U.S.	Forest fire	7–130	–	0.012	[44]
Yucatan, Mexico	Biomass burning	–	–	0.022	[45]
California, U.S.	Biomass burning	–	–	0.019	[46]
Colorado, U.S.	Wildfires	<150	–	0.027	[47]
the Flint Hills, U.S.	Grassland fire	–	95	–	[48]
Western U.S.	Wildfires	>400	–	–	[49]

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/atmos13020170/s1: Figure S1: Influence of different ntree and mtry values on RF model; Figure S2: Performance of the RF model in predicting the NH₃ concentrations; Figure S3: Temporal variation of hourly concentrations of PM_2.5, SO₂, NO_x, and CO; Figure S4: Hourly variation of meteorological parameters; Table S1: Summary of 11 RF models and their predictors in this study; Table S2: Model validation in 11 RF models in this study; Table S3: Correlation matrix of NH₃, other air pollutants and meteorological parameters during the burning period; Table S4: Observed and predicted concentrations of NH₃ at the Xianghe site during the burning period; Table S5: Range and average of the ratio between NH₃ and other air pollutants before and during the burn event.

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