# Two-Minute Onsite Detection of Agricultural Water Nitrogen Pollution with an Ammonia Gas Sensing System

Chih-Lu Chiang, Yu-Yu Huang, Te-Yao Liu, Yi-Yu Chen, Olivier Soppera, Li-Yin Chen, Hsin-Fei Meng, Hsiao-Wen Zan

► To cite this version:

Chih-Lu Chiang, Yu-Yu Huang, Te-Yao Liu, Yi-Yu Chen, Olivier Soppera, et al., Two-Minute Onsite Detection of Agricultural Water Nitrogen Pollution with an Ammonia Gas Sensing System. ACS ES&T Water, 2025, 5 (6), pp.2965-2974. 10.1021/acsestwater.4c01143. hal-05371781

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Two-Minutes Onsite Detecting Agriculture Water Nitrogen
Pollution with Ammonia Gas Sensing System

Chih-Lu Chiang<sup>a,c,d</sup>, Yu-Yu Huang<sup>b*</sup>, Te-Yao Liu<sup>c</sup>, Yi-Yu Chen<sup>c</sup>, Olivier Soppera<sup>d</sup>, Li-Yin Chen<sup>c</sup>,
Hsin-Fei Meng<sup>e*</sup>, Hsiao-Wen Zan<sup>a,c*</sup>

<sup>a</sup> Institute of Pioneer Semiconductor Innovation, Industry Academia Innovation School, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan.

<sup>b</sup> Agriculture Technology Translation Center, Taiwan Agriculture Research Institute, Ministry of Agriculture, Executive Yuan, Taichung 413008, Taiwan.

<sup>c</sup> Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan.

<sup>d</sup> Mulhouse Institute for Material Sciences, CNRS LRC 7228, BP2488, Mulhouse 68200, France.

<sup>e</sup> Institute of Physics, College of Science, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan.
KEYWORDS. ammonia, agriculture, nitrogen fertilizer, nitrogen pollution, gas sensing.

ABSTRACT. Nitrogen pollution from agriculture is one of the major environmental problems. A high proportion of the fertilizers leached into the environment by runoff water, causing eutrophication and biodiversity damage. This study employed a semiconductor ammonia gas sensor to detect total ammonia nitrogen (TAN) pollution through fertilization, as ammonia emissions correlate with the aqueous ammonium ion concentration. The sensor has a vertical conduction channel and high sensitivity. The gas method is compared with the conventional Indophenol method. It shows consistent results from the drinking water criterion 0.5 mg/L to the acute criterion 17 mg/L of nitrogen pollution. The gas method is more convenient. For a given nitrogen dose, comparing two kinds of popular fertilizers, ammonium sulfate and urea, the lower ammonium concentration for urea is discovered. This gas sensor was applied to track the TAN runoff of the rice field to reveal that, after base fertilization, there is an ammonium shock exceeding regulations. Therefore, real-time monitoring is necessary to assess the time-dependent environmental impacts and develop optimized fertilization schedules, considering the cost of fertilization and the environment. The gas sensor is an ideal tool for these approaches to control nitrogen pollution.

SYNOPSIS. We successfully used an ammonia gas sensing system to measure water ammonia nitrogen pollution on-site within 2 minutes.
Rainfall Ammonia Runoff
Fertilizer Type: (NH₄)₂SO₄/urea / background (no fertilizer)

<figure><img src="image_2.png" /></figure>

On-Site Gas Detection
Size: fits in a 20-inch luggage Power Consumption: 42 Wh

<figure><img src="image_3.png" /></figure>

Paddy Field Ammonia Runoff
Day 0 : fertilization day
Day 7 : one day before transplanting

<figure><img src="image_4.png" /></figure>

# 1. INTRODUCTION

Eutrophication has become an increasingly prevalent problem across the world. Harmful algae and turbid waters caused multiple issues: drinking water safety, reduced aqua ecosystem biodiversity, and dead zones in coastal areas resulting from hypoxia¹⁻³. Some reservoirs suffer from severe eutrophication and algae bloom. The application of algaecide generated toxic effects on non-targeted aquatic organisms⁴ and produced residual chemicals⁵, such as heavy metals and toxins, which may cause health risks. Nitrogen compounds in the water are the main origins of eutrophication. Nitrogen is the primary factor for crop production. Large inputs of N-fertilizers are applied to enhance the yields of major crops for modern agriculture⁶,⁷. In particular, ammonia is primarily produced directly from nitrogen in the air using the Haber-Bosch method⁸. Ammonia is the primary nitrogen fertilizer, constituting over 60% of the total fertilizer supply⁹. Besides, it is one of the preferred nutrients for cyanobacteria, a toxic algae type¹⁰,¹¹.

Due to population growth and global demand for higher food quality, the increased use of nitrogen fertilizers is inevitable. Agriculture, therefore, becomes the principal contributor to nitrogen water pollution in many countries, such as China, Western Europe, and the United States¹²⁻¹⁴. Agricultural water runoff, accounting for 16.43-23.07% of agriculture N loss¹⁴, is the largest source of eutrophication¹,¹⁴⁻¹⁶. Regulating and controlling agricultural nitrogen pollution are urgent environmental
actions for protecting biodiversity and water quality. The development of an effective and convenient TAN detection method is crucial.

Most conventional ammonia detection methods (Table 1) are typically conducted in laboratories to ensure precise results. The ion chromatography method (IC) has little interference and a wide working range¹⁷⁻¹⁸. However, the equipment, such as high-performance liquid chromatography (HPLC) and proton nuclear magnetic resonance (H-NMR), can be costly and require highly skilled operators. Colorimetric and fluorescence methods typically employ pH or ammonia-reactive reagents¹⁹,²⁰. Sample dilution is required since the highest operation concentration is lower than the acute criteria for nitrogen water pollution (17 mg/L)²¹. To reach high sensitivity, they often require pre-treatments to eliminate interference. For instance, when reducing salinity in water samples, the pH should be modified to release the volatile ammonia and re-dissolve into the water²²⁻²⁴. Furthermore, runoff pollution cannot be detected on site. In that case, tracing the

The source of ammonia pollution, which is used to convince farm owners immediately to change their fertilization strategies with credible and real-time data, can be challenging.

Ion-selective electrode methods (ISE)²⁵⁻²⁷ and microfluidic paper-based analytical devices (μPADs)²⁸⁻³⁰ have been developed for on-site determination. Both offer high sensitivity, rapid sensing, compact dimensions, and minimal reagent consumption. Nevertheless, these methods require pretreatments to diffuse and re-dissolve the sample to enhance precision.

<table><caption>**Table 1.** Ammonia detection method comparison.</caption><thead><tr><td>Method</td><td>Sensing Range (mg/L)<sup>Note 1</sup></td><td>Interference</td><td>Pre-treatment</td><td>Sensing Time (/batch)</td><td>Machine</td><td>Ref.</td></tr></thead><tbody><tr><td>Ion Chromatography Method</td><td>0.05-500</td><td>-</td><td>Filtration, analyte separation machine regeneration</td><td>12-22 mins</td><td>HPLC/H NMR<sup>Note 2</sup></td><td>[17,18]</td></tr></tbody></table>
<table><tr><td>Indophenol Method</td><td>0.01-2</td><td>pH, Cl<sup>-</sup>, Ca<sup>2+</sup>, Mg<sup>2+</sup>, turbidity, H<sub>2</sub>S</td><td>Distillation, chemicals removed interference, buffered, reagents preparation</td><td>1 hr</td><td>Spectrometer</td><td>[19]</td></tr><tr><td>OPA<sup>Note 3</sup> Method</td><td>2.8</td><td>Amines, pH, flow rate</td><td>15 minutes set-up, chemicals removed interference, dilution, buffered, reagents preparation</td><td>6 mins</td><td>Continuous Flow Analyzer/ Spectrometer</td><td>[19]</td></tr><tr><td>Ion-Selective Electrode Method</td><td>0.05-17000</td><td>Cl<sup>-</sup>, amines, Hg<sup>2+</sup>, Ag<sup>+</sup>, solvent ions</td><td>Remove</td><td>2-3 mins</td><td>Electrode Meter</td><td>[25-27]</td></tr><tr><td>μPAD</td><td>0.5-10</td><td>-</td><td>-</td><td>1-5 mins</td><td>μPAD</td><td>[28-30]</td></tr><tr><td>OSC based Gas Sensor (this work)</td><td>0.3-40</td><td>pH</td><td>pH treatment</td><td>2 min</td><td>Gas Measurement System</td><td>-</td></tr></table>

Note 1. Acute Criteria: 17 (mg/L), Chronic Criteria: 1.9 (mg/L), Drinking Water Standard: 0.5 (mg/L) (US EPA, 2013²¹)

Note 2. HPLC: high-performance liquid chromatography, H NMR: Hydrogen Nuclear Magnetic Resonance Spectroscopy

Note 3. OPA: o-phthaladehyde

Note 4. μPAD: Microfluidic Paper-based Analytical Devices

All the methods above depend on directly detecting ammonia in the water. In principle, the aqueous ammonia concentration can also be obtained by detecting ammonia emitted into the gas phase. The gas sensor is usually much faster and more convenient than the liquid sensor. So far, no other groups are reporting on applying gas sensors for ammonia water pollution, particularly in the field. Developing an ammonia gas detection method that integrates an ultrasensitive gas sensor and a real-time measurement system could be promising for environmental protection.

This work is dedicated to developing a new concept for on-site, real-time detection of ammonia wastewater pollution using ammonia gas detection. The ammonia sensor system utilized an organic semiconductor (OSC) with ppb-level sensitivity³¹. Previously, it has been successfully applied in detecting
chronic kidney disease³² and fish freshness³³. In this work, the measurement system³¹⁻³³ is incorporated with a water collection device to make volatile ammonia gas flow directly into the system, the measurement can be done within 2 mins. Preliminarily, we demonstrated the proposed concept in lab but suffered from pH interference in acid water source³⁴. Here, we conducted a complete investigation to alleviate interferences from pH level, ions, and temperature. Successful field studies verified dynamic detection range from drinking water (0.5 mg/L)²¹, chronic pollution (1.9 mg/L)²¹, to acute pollution (17 mg/L)²¹. The proposed gas detection and the conventional indophenol colorimetric methods gave consistent results with high correlation coefficients in standard rainfall (R²=0.99) and paddy field (R²=0.91) runoff collections. Additionally, the system was downsized to be portable and unplugged, making ammonia wastewater sensing surrounding agricultural activities more convenient and efficient.

## 2. METHODOLOGY

### 2.1 Soil and Fertilizers.
The soil samples used in the experiments were collected from the Wufeng, Taichung, Taiwan fields. The soil samples were air-dried, sieved to a particle size less than 2 mm, and thoroughly mixed before use. The soil characteristics are outlined in Supplementary Table 2.

According to the fertilizer used for the lettuce in the Crop Fertilizer Handbook in Taiwan³⁵, the N-P-K ratio of the fertilizer was 60: 100: 60 (kg/ha), with calcium phosphate used as the source of phosphorus fertilizer and potassium chloride used as the source of potassium fertilizer. Two common elemental fertilizers, ammonium sulfate and urea, were used as nitrogen sources to investigate the impact of different nitrogen fertilizers on ammonia nitrogen pollution. A control group without adding fertilizer was used for comparison.
**2.2 Ammonia Fertilizer Solution Preparation.** The preparation of a 1000 mg/L ammonium standard solution was as follows. 4.762 g of ammonium sulfate fertilizer was weighed, dissolved in a small amount of water, transferred into a 1000 mL flask, and then filled to 1000 mL with RO water to ensure thorough mixing. The ammonia fertilizer solutions used in the experiments and the interference analysis were prepared by diluting the 1000 mg/L standard ammonium solution with pure RO water as the solvent. The indophenol colorimetric method was used as a control. Before gas sensing, the collected samples were thoroughly homogenized. Then, 50 mL of the sample was placed in a 150 mL glass flask for gas measurement. Before gas measurement, 2 mL of 8 M NaOH was put into the glass flask's inlet with a 5-second stirring.

**2.3 Standard Rainfall Runoff Collection.** The rainfall simulator (Figure 1(a)) was referred to previous researches³⁶,³⁷. The study constructed a rainfall machine enclosed by an aluminum frame (200 cm length, 100 cm wide, 323 cm high) and equipped with a 50 WSQ nozzle (Spraying Systems Co. USA), a pressure regulator, a flow meter, and a valve gauge to control the rainfall intensity, which was set at approximately 90 mm/hr³⁸. It was connected externally to a bucket filled with irrigating water for the water supply.

An acrylic runoff box, as illustrated in Figure 1(b), was fabricated by the previous studies³⁸. The box is 1 meter in length and 20 centimeters in width, featuring three side walls, each measuring 7.5 centimeters in height, and one side wall standing at 5 centimeters high, specifically designed for runoff collection³⁸. A V-shaped groove was carefully incorporated into the 5-centimeter side wall to enhance runoff collection efficiency. The box's base has nine drainage apertures, each boasting a diameter of 5 millimeters. Soil was introduced to a height of 4 centimeters within the
box. Then, fertilizers were poured out evenly onto the surface of the soil. Subsequently, an additional 1-centimeter layer of soil was added, resulting in an average height of 5 centimeters.

To commence the runoff collections, the runoff box should be placed at the center of the ground inside the rainfall simulator. Timing started from the moment water began flowing into the container, and runoff was collected for a total of 30 minutes. Every 5 minutes, a new container was used, resulting in a total of 6 containers collected, and they can be sequentially numbered as S1-S6.

<figure><img src="image_5.png" /><figcaption>**Figure 1.** Real Photos. (a) runoff box, (b) rainfall simulator, (c) paddy field, Ammonia Detection System: (d) schematic diagram, (e) real photo.</figcaption></figure>

**2.4 Paddy Field Runoff Collections.** To assess the feasibility of ammonia gas detection for practical use, the collections were conducted in some 0.5 ha Taiwan Agriculture Research
Institute (TARI) research farms in July-August 2023. TARI is located in Wufeng District, Taichung City (24°03 N, 120°69 E, altitude of 85 m). The area features a tropic humid climate according to the Thornthwaite System, with an annual average temperature of 19.4°C. The highest temperatures are typically observed from May to September, and most of the precipitation in this region occurs during the summer, accounting for over 75% of the annual rainfall. The annual rainfall ranges from 1,400 to 1,670 millimeters³⁹. The fields were used to grow Tainung 71 (Yichuan aromatic rice), and cultivation was conducted twice a year. Figure 1(c) shows the paddy field's real photo.

Paddy field runoff sample collection was conducted twice: 1 hour after the fertilizer application, referred to as "Day 0", and the other one day before the rice transplanting, named "Day 7". The procedure for fertilizer application was as follows: the paddy fields were flooded (with a water depth of 1 cm) before fertilization. To maintain the water depth, irrigation would be conducted if the water depth was lower than 1 cm due to evaporation or leakage. Then, 800 kg/ha of compound fertilizer (Compound fertilizer No. 43 (Taiwan Fertilizer Co., Ltd.)), whose N-P-K ratio is 15:15:15, was applied using a tractor fertilizer spreader. The measurements were conducted 1 hour later to understand the ammonia pollution caused immediately after fertilization. Drainage was carried out before the rice transplanting to keep the rice sprouts from floating away. Therefore, the measurements were conducted to investigate the pollution in the drainages.

On either Day 0 or Day 7, four samplings were conducted at random locations within the drainage channel of each field. Every sample was collected using a plastic canister. The gas measurement system was put in a 20-inch suitcase for on-site measurements. As a comparison,
samples were simultaneously transported back to the laboratory nearby and analyzed using the indophenol method.

**2.5 Ammonia Gas Detection System.** Figure 1(d)(e) depicts the measurement system used in this study. The system comprises the following components.

The previous research referred to an ammonia gas sensor (Taiwan Patent)³¹. The sensing layer of the chip is PTB7 (poly[(4,8-bis(2-ethylhexyloxy)-benzo-(1,2-b:4,5-b)dithiophene)-2,6-diyl-alt-(4-(2-ethylhexyl)-3-fluorothieno[3,4-b]thiophene)-2-carboxylate-2,6-diyl)]), which is a p-type sensing material³¹. The details of the sensor performance, including stability and selectivity, were reported in our previous publication³¹. Effective ammonia gas concentration was determined by analyzing the current variation ratio, defined as the sensing response, according to equation (1). The gas injection duration was set as 30 seconds. A calibration curve of the chip will be established by using ammonia gas with known concentration.

$$Response (\%) = \frac{|I_{final} - I_{initial}|}{I_{initial}} \times 100\% \quad -eq. (1)$$

A 150 ml glass flask with two inlets holds 50 ml of sample water. One inlet is connected to the system to introduce volatile ammonia gas, while the other inlet adsorbs carrier gas to maintain vapor pressure. The latter inlet is connected to a desiccation cylinder filled with activated carbon to purify the air and prevent contamination from ambient ammonia. A desiccation cylinder filled with NaOH eliminates water moisture, ensuring that ambient humidity does not interfere with the sensing process. The relative humidity should be stabilized at around 10%. A digital flow meter controls the gas flow at 0.50 liters per minute throughout the research, driven by an air pump. A glass chamber houses the sensing chip. To maintain airtightness and shield external light, precautions are taken. The sensing chip connects to the signal processing equipment via
two wires: one for the positive and one for the negative electrodes. An electrical signal processing equipment (Keysight U2722A USB Modular Source Measure Unit, Keysight Technologies, Santa Rosa, CA, USA) supplies a voltage, typically 5V. It detects the chip's current, typically from 10⁻⁵ to 10⁻⁶ A. The current signal is transmitted in real-time to a computer through a USB cable. A pad is equipped with software that displays the current and response of the chip, recording them for subsequent signal processing. The electrical processing equipment (u2722a) consumed the most power, requiring bringing a 12V portable charger and assembling the system in a 20-inch suitcase for outdoor measurements. In the future, the electrical readout can be replaced with a commercial IC to save power and greatly reduce the size, making the system more suitable for portable detection.

The system detects ammonia gas volatilized from pH-treated samples. The detailed procedure including sample handling and gas measurement is given in Supplementary Method 2 to show the total 2 minutes operation. To compare ammonia gas sensing methods and establish a regression equation for converting detection results to TAN concentration in water, samples were also detected by the indophenol method. The indophenol method followed the protocol from previous research³⁹ and is detailed in Supplementary Method 1. A regression line was determined via linear regression based on the detection results of both methods. If considering a close system with infinite time to reach equilibrium, the regression slope can be derived from Henry's law as shown in eq. 2:

$$[TAN] = [NH_3 \cdot H_2O] + [NH_4^+] = H_{NH_3} \cdot p_{NH_3} \cdot (1 + K_{NH_4^+} \times 10^{14-pH}) \text{-eq. 2}$$

Where [TAN], [NH₃ · H₂O], and [NH₄⁺] are TAN, NH₃, and NH₄⁺ concentrations in water. pNH₃ is the partial pressure of gas NH₃ in atm, HNH₃ is the Henry's law coefficient for NH₃ as 62 M.
$atm^{-1}$ at 298 K⁴⁰. At high pH as 13, the $[NH_4^+]$ is negligible and $[TAN] = [NH_3 \cdot H_2O]$, allowing the regression slope $\frac{p_{NH_3}}{[TAN]}$ equals 948 (ppb/(mg/L))at 298 K. In our system, there is a carrier background flow with a fixed flow rate (0.5 L/min) to bring the volatile ammonia into the sensing chamber. The regression slope will be investigated and discussed in next section.

## RESULTS & DISCUSSION

### 2.1 Use Ammonia Fertilizer Solution to Verify the System Efficacy.

We first characterize the sensing characteristics of the proposed system and study the interference factors. To control nitrogen pollution, ammonia fertilizer solutions were prepared in lab to investigate the correlation between our ammonia gas detection methods and the standard indophenol method. Interference effects such as pH level, ions, and temperature were also studied and suppressed. Details are given below.
<figure><img src="image_6.png" /><figcaption>**Figure 2.** Ammonia fertilizer solution gas detection and interference analysis (a) Correlation between detected gas concentration and ammonium levels (indophenol method). (b) Sensor response for 0.2–20 mg/L ammonium. pH effect on gas detection at (c) 2 mg/L and (d) 10 mg/L. (e) Influence of KCl concentration on gas sensing. (Standard deviations were obtained from 3 independent samples.)</figcaption></figure>

To validate the concept of detecting ammonium in RO water using volatile ammonia gas, various concentrations of ammonia fertilizer solution were prepared (20 mg/L, 10 mg/L, 4 mg/L, 2 mg/L, 1 mg/L, 0.6 mg/L, 0.2 mg/L). Each sample was measured by gas sensing and indophenol methods, and the relationship between the two results is illustrated in Figure 2(a). Pretreatment to adjust the sample pH level (pH 13) is described in the Method section and is discussed in the next paragraph. The detected effective ammonia gas concentration was determined as follows. The real-time gas sensing to detect the volatile gas from the solution samples is illustrated in Figure 2(b). The yellow regions represent the detection of sample volatile gas for 30 seconds, and
the white background regions represent the removal of sample gas by purging the system with ambient gas. In the yellow region, the current variation ratio (i.e., the current drop divided by the initial current value) is defined as the sensing response. The sensing response level increased as the ammonium concentration in the fertilizer solution increased. The response is then compared with the ammonia gas calibration curve (Supplementary Figure 1) to give an effective ammonia gas concentration for every fertilizer solution sample.

In Figure 2(a), the unit of effective ammonia gas concentration was parts-per-billion (ppb), measured with an ammonia gas detection method, while the unit of ammonium concentration in water was mg/L, detected by the indophenol method. The regression line (y = 266x + 7) exhibits a strong correlation (R²=0.99) and indicates that the effective ammonia concentration (ppb) was approximately 266 times the concentration of ammonia in water (mg/L). The intercept (7 ppb) was below the gas detection limit and close to 0. Furthermore, the detection limit was defined by the ammonia gas sensor to be 50 ppb, which refers to 0.2 mg/L ammonia concentration in water. The slope is approximately 30% of the value 948 calculated by Henry's law, indicating that our open system with a constant flow reduces the volatile ammonia concentration but still successfully forms a steady state condition to transform TAN into gas-phase ammonia. Therefore, with controlled flow rate and pressure, the system provides a good approximation for TAN concentration, which is also the quantity referred by EPA guidelines. The working range was thus sufficiently wide to determine 0.2 to 20 mg/L ammonium pollution. We noticed that the sensitivity and dynamic range of the proposed ammonia gas sensor system are suitable for detecting the pollution water ranging from drinking (0.5 mg/L)²¹ chronic (1.9 mg/L)²¹ to acute (17 mg/L)²¹ levels.
The pH interference analysis aims to ensure ammonia gas detection for reliable results in various environments. However, we faced a pH interference issue at acute pollution levels in our previous work³³. Therefore, for the analysis, two kinds of ammonia fertilizer solution, 2 mg/L and 10 mg/L were used, and a 4% solution of 8 M NaOH (2ml compared to 50ml sample solution) was employed to modify all samples to exhibit pH 13. In specific environments, the water body may become acidic or alkaline; for example, acid rain typically results in a pH of approximately 4-5, and swift algae proliferation leads to a notable rise in pH to 9-10⁴¹. Therefore, it is crucial to analyze samples with various pH levels to evaluate the efficacy of the proposed methods. This work used acetic acid to prepare acidic samples with pH levels of 4 and 6, while NaOH was used for alkaline samples with pH levels of 10 and 12. Figure 2 (c), (d) presents the analysis results with 2 mg/L and 10 mg/L ammonia fertilizer solution, demonstrating that ammonia gas detection provided stable and repeatable results across varying pH values. Consequently, irrespective of the ammonia concentration in each sample, we effectively deliver a reliable sensing calibration curve without pH level interference.

The ion interference analysis aims to ensure that the proposed ammonia gas detection is not affected by other ions in the sample water. Potassium (K) is one of the primary fertilizer elements and significantly influences the performance of ion-selective electrodes³⁸, so it has been selected as the target of this study. Typically, the fertilizer ratio of nitrogen (N) to potassium (K) is 1:1 (kg/ha), with ammonium sulfate containing approximately 21% of N and potassium chloride containing approximately 60% of K. Therefore, if the proportion of ammonium sulfate to potassium chloride is 1:1 (kg/L), the ratio of N to K becomes 1:0.6 (kg/L). Figure 2(d) illustrates the interference observed in 2 mg/L ammonium sulfate solutions with varying concentrations of
potassium sulfate (0, 2, 4, 6, 8, 10 mg/L). Each condition closely resembles real-world agricultural runoff conditions. The test results (Figure 2(d)) suggest that ions do not affect the ammonia gas detection results, revealing the advantage of the proposed method when being compared with the ion-selective electrodes.

Since Henry's law coefficient is temperature-dependent, we preliminarily evaluated the influence of solution temperature by placing the liquid samples on hot plate at 35°C. Compared to samples at 25°C, no significant difference in effective ammonia concentration was observed (Supplementary Figure 2). Noted that the ambient air was at 25°C and the system was open with constant airflow, internal temperatures should lower. Future studies to carefully evaluate ambient temperature effects are still needed. In this work, we measured under shadow to suppress ambient temperature effects.

**3.2 Standard Rainfall Runoff Experiments to Investigate Fertilizer Effect.** In this section, we experimented in the field with the standard rainfall simulator (Figure 1(a)) and standard runoff boxes (Figure 1(b))³⁶. The setup and measurement procedure details were described in Method section. We collected rainfall runoff water samples to confirm further the function of the proposed ammonia gas detection system. To mimic the real farm's condition and understand the impact of fertilization on ammonia fertilizer solution pollution, 30-minute duration standard rainfall runoff experiments were conducted after the base fertilizer addition (N-P-K of the fertilizer was 60-100-60 (kg/ha)).

The experiment employed two nitrogen fertilizers, ammonium sulfate, and urea, to discuss the influence of different nitrogen fertilizers. The control group had no fertilizer addition. All
measurements, including ammonia gas detection and the indophenol method, were conducted
in a laboratory after the sample collection. It is noted that the sensing system does not respond
to pure water, ensuring that the sensing signal reflects the effect of other components in the
sample solution. As the rainfall duration was 30 minutes and samples were collected every 5
minutes, each sample collection can be sequentially named S1 to S6. For instance, S1 corresponds
to the sample collected during the first 5 minutes of the runoff experiment's 30-minute rainfall
duration.

The simulated runoff results (Figure 3) show samples S1 to S6. Sample S1 corresponds to the runoff occurring within the first 5 minutes hence a notably high ammonia concentration was observed. Ammonia concentration substantially decreased from S1 to S2, gradually declining in the subsequent S3-S6 samples. From Figure 3(a), the research demonstrated direct pollution when using ammonium sulfate as the nitrogen fertilizer. During the initial 5 minutes, the pollution exceeded acute criteria and surpassed chronic criteria in S2-S6. The measured values only last 30 minutes and should not be interpreted as exceedances of the acute (1-hour) or chronic (30-day) criteria. Although the base fertilizer rate obeyed the rule established by the TARI, the 30-minute runoff duration could still impact the lake while discharging. From Figure 3(b), urea fertilizer, otherwise, had a lower impact on pollution. S1 exceeded chronic criteria, S2 fell between chronic criteria and drinking water standards, and S3 kept to a declining trend. S4-S6 were all below drinking water standards. The two methods follow the same trends. In Figure 3(c), the control samples without adding fertilizer (background) exhibited ammonium concentrations lower than the drinking water level.
As demonstrated in previous studies<sup>42,43</sup>, the hydrolysis of urea takes several hours to a few days. Previous research<sup>42,43</sup> indicated urea produces lower runoff ammonia nitrogen pollution because it needs hydrolysis to generate ammonium. This hydrolysis reaction requires urease, an enzyme that soil bacteria produce, effectively making urea a slow-release fertilizer. In our experiment, artificial rainfall was initiated immediately after fertilization, giving urea insufficient time to convert into ammonium nitrogen. Therefore, the response was significantly less than that of ammonium sulfate fertilizer. Even though there might still be a significant amount of undecomposed urea in the runoff to gradually generate ammonia nitrogen, it is undeniable that urea does not cause short-term high concentrations nitrogen shock.

<figure><img src="image_7.png" /><figcaption>(b) Urea</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(c) Background (No Fertilizer)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(d) All Detection Results</figcaption></figure>

<figure><img src="image_10.png" /></figure>
**Figure 3. Standard rainfall runoff experiments.** Runoff analysis from (a) ammonium sulfate, (b) urea, and (c) control (no fertilizer). (d) Correlation between volatile ammonia and ammonium concentrations. (Standard Deviations were obtained from 3 independent samples.)

**3.3 Paddy Field Runoff Collections to Reveal Contamination Issue.** To assess the practical viability of deploying the system outdoors, runoff from a real paddy field planting rice was collected, and on-site real-time measurements were conducted.

The cultivation of rice takes approximately three months. Rice is transplanted from the nursery site to the paddy field about one month after planting. The standard fertilization process is a two-step fertilization. First, basal fertilization is applied, which is simply interpreted as "Day 0". Then, the rice seedlings are transplanted after eight days, interpreted as "Day 8". Then, if the growth of the rice seedlings is not as expected seven days after transplantation, additional topdressing is applied. Both two fertilizations occur during the rice's growing stage⁴⁵. In this experiment, we investigated nitrogen pollution due to basal fertilization. For the water level of the paddy field: the paddy fields were flooded (with a water depth of 1 cm) before fertilization. To maintain the water depth, irrigation would be conducted if the water depth was lower than 1 cm due to evaporation or leakage. The field is drained a day before transplanting to prevent the rice from being washed away, interpreted as "Day 7". As shown in Figure 4(a), the on-site real-time detections were performed on Day 0, the day right after base fertilization, and Day 7, the day to drain the field. The study involved three neighboring paddy fields (Figure 4(b)): Field#1, Field#2, and Field#3 inside the Taiwan Agricultural Research Institute. Every field had the same fertilizer application (800 kg/ha of compound fertilizer No. 43 (Taiwan Fertilizer Co., Ltd.)), N-P-K of the
fertilizer was 120-120-120 (kg/ha)) and field area (0.5 ha). For every field, samples were collected
from four different sites along the drainage of the 3 fields (total 12 sample sites). The ammonia
gas detection and the indophenol method were conducted right after the collection. Additionally,
the ammonia gas detection was conducted just on the ridges of the paddy fields within 30
minutes after sample collection. The Indophenol method takes about 40 minutes to filter, so the
measurement was conducted within 1 hour after sample collection at the lab in TARI.

In Figure 4(c), samples from the 12 sample sites on Day 0 and Day 7 were measured by our gas sensing system and the indophenol method. On Day 0, nearly half of the samples exceeded acute criteria, which may cause environmental problems and unpleasant odors for nearby residents².

Noted that in future we should conduct continuous 1-hour sampling with measurement to confirm the 1-hr lasting acute pollution condition. On Day 7, a significant ammonia concentration decreased was observed. All 12 samples showed that the ammonia concentrations on Day 7 fell below drinking water standards, indicating a lack of pollution. This tells that acute pollution occurs shortly after base fertilizer, serving as an ammonia nitrogen shock to the environment. If the detection is not conducted on time, we will not see this pollution. This result also supports that two-times low-dose fertilization instead of one-time high-dose fertilization may be more environmentally friendly.

The reduction of ammonia nitrogen pollution in paddy fields can be due to several reasons⁴³.
Since the water depth in paddy fields was maintained constantly, this reduction may result from
fertilizer loss into the trench due to heavy rainfall events during those days. The physical and
chemical reactions with NH₄⁺ in soil, such as cation exchange, fixation by clay lattice, microbial
immobilization, and rapid nitrification, may also cause ammonia nitrogen reduction⁴⁵. The total
24 samples were depicted in Figure 4(d) to investigate the relationship between gas detection and indophenol detection, with a high R²=0.91, the slope of the experiments was 295, which was higher than but close to those observed in the ammonia fertilizer solution (266 in Figure 2 (a)) and the standard rainfall runoff experiments (279 in Figure 3(d)). The intercept of the experiments was 330 ppb, which was higher than those observed in the ammonia fertilizer solution (7 ppb in Figure 2 (a)) and the standard rainfall runoff experiments (21 ppb in Figure 3(d)). In indophenol measurement, we did filtration to avoid light scattering interference. If the samples contained suspended clay lattice⁴⁶,⁴⁷ particles, they may contain attached ammonia nitrogen to release volatilized ammonia upon adding alkali. On the other hand, in indophenol method with sample filtration, the ammonia gas from suspended particles could not be measured. This suspected sample difference is due to suspended particles; however, it must be further verified in future works. In this work, even with the intercept as 330 ppb, it does not affect the detection of high level TAN in Day 0 measurement.

Rice is a staple in Asia, and fertilization practices in Taiwan are similar to those in other countries (Supplementary Table 1). For example, China's base fertilizer rate (105-180 kgN/ha) is even 1.5 times that in Taiwan (90-120 kgN/ha). In our work, on the day of base fertilization, almost half of the sites showed high TAN levels. This indicates that conventional fertilization conditions may have an acute environmental impact, likely affecting the ecosystem. Moreover, this pollution situation completely dissipated within a week, highlighting the importance of real-time onsite environmental monitoring. Without immediate detection results, it is impossible to accurately capture the critical moments of severe pollution, thus hindering the government's ability to implement more environmentally friendly fertilization policies.
<figure><img src="image_11.png" /><figcaption>**Figure 4. Paddy Field Runoff Collections** (a) timeline (b) paddy fields schematic diagram (c) ammonia gas detection results (d) correlation between ammonia gas detection & indophenol method.</figcaption></figure>

## 4. CONCLUSIONS

To confirm that the proposed method can produce accurate results in the field, we conducted 3-stages complete experiments to verify this technology's accuracy and detection capabilities. We first started with the fertilizer water solutions configured in the laboratory. We obtained good results. Secondly, we then went to the farm to build a standard runoff box and sprinkler device. We used different fertilizers to measure the ammonia nitrogen pollution of the runoff. Finally, we took the portable measurement system directly to the field ridge to measure the ammonia nitrogen pollution of the runoff. In paddy fields under actual cultivation, water ammonia nitrogen pollution in surrounding ditches was measured on the first day of fertilization (Day 0) and one week after (Day 7). In the three stages of measurement, the samples we obtained were all analyzed using the standard indophenol colorimetric method. After comparison, it was confirmed that the proposed measurement device could obtain similar results to the indophenol
colorimetric method (the correlation coefficients of the 3 stages are 0.99, 0.99, and 0.91, respectively.).

The most prominent feature of this new measurement method is that the correct results can be obtained immediately (2 mins) in the field. Secondly, the stable system and sensitive ammonia sensing chip allow the detection dynamic range to cover the three TAN pollution levels in the US EPA standard²¹, including drinking water, chronic pollution, and acute pollution.

We will continue to develop subsequent developments of the sensor system. In this work, it is already a mobile device in the form of a cart powered by batteries. Many oceans, reservoirs, and lakes suffer from eutrophication because of local agricultural activities. Real-time data over large areas are needed to track upstream and identify the major sources of nitrogen pollution. Noted that TAN pollution may also come from non-agricultural sources such as livestock and industry, real-time measurement enables gradient mapping to help identify pollution sources. Future studies include testing with additional fertilizer types such as organic and manure-based fertilizers or with non-agricultural sources will be conducted to assess the sensor's performance across diverse inputs. We will also evaluate stability and accuracy under real-world conditions like rain, wind, dust, and UV exposure. In the future, we aim to produce an automatic sampling tool and adjust the electric signal circuitry to realize a remotely controlled monitoring system. We hope to present more field data so that government agencies and the general public can see the impact of existing fertilization strategies on the environment. Even more, farmers' widespread use of this tool, supported by incentive policies, could enable localized tracking of TAN data and promote region-specific green fertilization practices. Nitrogen shock caused by fertilizer dumping is a hidden ecological killer. With real-time detection tools, pollution curves
should be established, and green and sustainable fertilization strategies that properly consider
cost and yield should also be proposed to benefit our future generations.

ASSOCIATED CONTENT

The following files are available free of charge: ES&T-Supporting Information. Overview of
additional experimental results, charts and the details of the control method. (pdf)

AUTHOR INFORMATION

**Corresponding Author**

**Hsiao-Wen Zan** – Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan; or- cid.org/0000-0002-7685-1245; Email: hsiaow-en@nycu.edu.tw

**Hsin-Fei Meng** – Institute of Physics, College of Science, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan; Email: meng@nycu.edu.tw

**Yu-Yu Huang** - Agriculture Technology Translation Center, Taiwan Agriculture Research Institute, Ministry of Agriculture, Executive Yuan, Taichung 413008, Taiwan; Email: yuyuh@tari.gov.tw

**Author Contributions**

Chih-Lu Chiang: Conceptualization, Investigation, Methodology, Formal analysis, Roles/Writing – original draft, Validation, Visualization; Yu-Yu Huang: Methodology, Resources and Validation; Te-Yao Liu: Conceptualization, Methodology; Yi-Yu Chen:
Methodology; Olivier Soppera: Supervision; Li-Yin Chen: Validation, Writing – review and editing; Hsin-Fei Meng: Validation, Writing – review and editing; Hsiao-Wen Zan: Conceptualization, Writing – review and editing, Supervision, Funding acquisition, Project administration.

## Funding Sources

This work was financially supported in part by the Co-creation Platform of the Industry-Academia Innovation School, NYCU, under the framework of the National Key Fields Industry-University Cooperation and Skilled Personnel Training Act, from the Ministry of Education (MOE), the National Development Fund (NDF), and industry partners in Taiwan.

Also supported by the Higher Education Sprout Project of NYCU and MOE, Taiwan.

## Notes

The authors declare no competing financial interest.

## ACKNOWLEDGMENT

This work was financially supported in part by the Co-creation Platform of the Industry-Academia Innovation School, NYCU, under the framework of the National Key Fields Industry-University Cooperation and Skilled Personnel Training Act, from the Ministry of Education (MOE), the National Development Fund (NDF), and industry partners in Taiwan.

Also supported by the Higher Education Sprout Project of NYCU and MOE, Taiwan.

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