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Article

The Accurate Inversion of the Vertical Ozone Profile in High-Concentration Aerosols Based on a New DIAL-A Case Study

by
Na Ma
1,
Jie Wang
2,3,*,
Chenglei Pei
4,
Sipeng Yang
5,
Tianshu Zhang
1,6,
Yujun Zhang
1,
Jianing Wan
7 and
Yiwei Xu
2
1
Institute of Environment, Hefei Comprehensive National Science Center, Hefei 230031, China
2
Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
3
Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China
4
Guangzhou Sub-branch of Guangdong Ecological and Environmental Monitoring Center, Guangzhou 510006, China
5
Guangzhou Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou 510180, China
6
Key Laboratory of Environmental Optics and Technology, Anhui Institutes of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
7
Wuxi Cas Photonics Co., Ltd., Wuxi 214028, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(16), 2997; https://doi.org/10.3390/rs16162997
Submission received: 18 June 2024 / Revised: 5 August 2024 / Accepted: 12 August 2024 / Published: 15 August 2024
(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)
Browse Figures
Graphical abstract
"> Figure 1
<p>Schematic diagram of the four-wavelength ozone lidar system.</p> "> Figure 2
<p>Optical properties of atmospheric aerosols: (<b>a</b>) profile of the atmospheric aerosol backscatter coefficient; (<b>b</b>) profile of the atmospheric aerosol extinction coefficient.</p> "> Figure 3
<p>Ozone concentration profile.</p> "> Figure 4
<p>Errors analysis caused by high-concentration aerosol: (<b>a</b>) the error distribution of backscatter coefficients; (<b>b</b>) the error distribution of extinction coefficients.</p> "> Figure 5
<p>(<b>a</b>) Error caused by the extinction term; (<b>b</b>) aerosol extinction coefficient.</p> "> Figure 6
<p>Concentration of ozone optimized with (After) and without (Before) aerosol consideration. (<b>a</b>) At 14:00 on 14 April 2021; (<b>b</b>) 07:00 on 15 April 2021; (<b>c</b>) 13:00 on 17 April 2021.</p> "> Figure 6 Cont.
<p>Concentration of ozone optimized with (After) and without (Before) aerosol consideration. (<b>a</b>) At 14:00 on 14 April 2021; (<b>b</b>) 07:00 on 15 April 2021; (<b>c</b>) 13:00 on 17 April 2021.</p> "> Figure 7
<p>Improvement rate of ozone concentration profiles before (black), during (red), and after (green) sand dust event.</p> "> Figure 8
<p>Time series of ozone concentration at 200 m retrieved with (red) and without (black) considering aerosol and ground level (blue).</p> "> Figure 9
<p>The correlation coefficients of between ground level and with (red) and without (black) considering aerosol.</p> "> Figure 10
<p>Vertical distribution of ozone observed using ozone lidar from 26 to 30 May.</p> "> Figure 11
<p>Comparison of the monitoring results between a ground -level automatic ozone analyzer and ozone lidar at different altitudes.</p> "> Figure 12
<p>Vertical profiles of ozone concentrations at different times on 28 May.</p> ">
Versions Notes

Abstract

:
Recently, in China, during the period of transition between spring and summer, the combination of sandstorms and ozone (O3) pollution has posed a significant challenge to the strategy of coordinated control of fine particulate matters (PM2.5) and O3. On the one hand, the dust invasion brings many primary aerosols and causes a large range of transboundary transport. On the other hand, the high concentration of aerosol causes a severe disturbance to the distribution of O3. Traditionally, high-resolution assessments of the spatial distribution of aerosols and O3 can be carried out using LiDAR technology. However, the negligence of the influence of aerosols in the process of O3 retrieval in traditional differential absorption lidar (DIAL) leads to an error in the accuracy of ozone concentration. Especially when dust transit occurs, the errors become bigger. In this study, a self-customized four-wavelength differential-absorption LiDAR system was used to synchronously obtain the accurate vertical distributions of ozone and high-concentration aerosol. The wavelength index of concentrated aerosol was inverted and applied to the differential equation framework for O3 calculation. This novel approach to retrieving the vertical profile of O3 was proposed and verified by applying it to a dust pollution event that occurred from April to May 2021 in Anyang City Henan Province, which is located in Northern China. It was found that the extinction coefficient of aerosol reached 2.5 km−1 during the dust period, and O3 was mainly distributed between 500 m and 1500 m. The O3 error exceeded over 10% arising from the high-concentration aerosol below 1.5 km during the dust storm event. By employing the inversion algorithm while considering the aerosol effects, the ozone concentration error was improved by over 10% compared with the error recorded without considering the aerosol influence especially in dust events. Through this study, it was found that the algorithm could effectively realize the synchronous and accurate inversion of high-concentration aerosols and O3 and can provide key technical support for air pollution control in China in the future.

Graphical Abstract">

Graphical Abstract

1. Introduction

In recent years, an increase in global tropospheric ozone has been observed. Studies on the maximum 8 h average concentrations of near-surface ozone in China, combined with lidar research on regional ozone concentrations and vertical distributions, have revealed significant spatial and climatic variations. During spring and summer, large regions in China face the challenge of both dust and ozone [1,2,3,4,5,6]. Moreover, ozone concentrations and distributions are significantly influenced by meteorological conditions and atmospheric transport [7,8]. Meteorological factors, such as temperature and wind speed, directly impact the rates of chemical reactions involved in ozone formation and decay, while atmospheric mass transport also affects the ozone spatial distribution [9,10,11]. These complex interactions necessitate comprehensive analyses using stereoscopic ozone monitoring data integrated with models of atmospheric dynamics and chemical processes [12].
Differential absorption lidar (DIAL) technology, which is renowned for its precise measurement capabilities for ozone and other gases, is extensively applied in ozone monitoring studies [13,14,15,16,17,18,19]. Specifically, multi-channel ozone DIAL systems offer new technical avenues for distinguishing between the physical and chemical properties of ozone and aerosols [20,21,22]. Nevertheless, the presence of aerosols introduces challenges for accurate ozone measurement, particularly in urban, industrialized areas and during pollution events, where their impact is significantly pronounced [23,24,25]. Recent research has increasingly focused on the interactions between aerosols and ozone, which influence the chemical life cycle of ozone and its optical measurement outcomes through scattering and absorption [26,27,28,29]. The importance of coordinated observation between ozone and aerosols is therefore undeniable. Innovations include the development of joint inversion algorithms that combine Raman scattering with DIAL technology to concurrently retrieve information about both ozone and aerosols [30,31]. Fernald’s discussion on the inversion of aerosol observational data under elastic scattering conditions highlighted these complexities [32]. The inversion of ozone profiles is further complicated by atmospheric inhomogeneity [33,34], cloud cover [35,36], geometric factor determination and aerosol interference [37], all of which require correction via advanced inversion algorithms to ensure the accuracy of ozone concentration measurements [38,39,40]. Despite numerous studies that have attempted to improve ozone-monitoring technologies, accurately inverting ozone during high-concentration aerosol events still poses significant challenges [41,42].
This research introduces an innovative algorithm that corrects ozone profile inversions by compensating for aerosol backscatter and extinction. This approach significantly reduces inversion errors, as demonstrated during a dust event in April 2021 and validated through an analysis of an ozone pollution event occurring from 26 to 30 May 2021. The findings not only enhanced the understanding of ozone pollution mechanisms but also provided more accurate scientific data for global environmental monitoring, climate modeling, and air quality management. Ultimately, these advances promote the application of remote sensing technology in environmental science and support the development of global ozone monitoring strategies.

2. Materials and Methods

2.1. Four-Wavelength Ozone Lidar

A four-wavelength ozone lidar system was designed to integrate transmitting, receiving, data acquisition, and control modules into a unified system. A diagram of the system architecture diagram is illustrated in Figure 1. The system used a Litron Nano DPSS laser (Litron Lasers Ltd., Rugby, UK). It emitted laser pulses at 532 nm with single-pulse energy of approximately 30 mJ and a repetition rate of 100 Hz. These pulses are directed into a sealed Raman tube with a reflective mirror. First-order Stokes pulses at 560 nm and second-order Stokes pulses at 590 nm were produced. These pulses were subsequently frequency-doubled to generate wavelengths of 280 nm and 295 nm.
The laser beams passed through a collimating lens, expanding them to a divergence angle of 0.2 mrad, which optimized their path into the atmosphere. In air, these wavelengths (280 nm, 295 nm, 560 nm, and 590 nm) engage in Rayleigh scattering with air molecules and Mie scattering with aerosol particles. A Cassegrain telescope with a diameter of 200 mm captured the backscattered echo signals from these interactions, focusing them onto a small-aperture diaphragm to minimize the impact of intense daylight interference. The receiving field of view (FOV) was approximately 1.5 mrad, with a receiving efficiency exceeding 20%. The signals were then passed through a filtering system and separated into distinct detectors for each wavelength by a grating spectrometer.
The detection methodology of the system utilizes a sophisticated combination of a high-speed module acquisition card (AD) and photon-counting techniques (PC). The AD acquisition card boasted a 12-bit resolution and 40 MHz sampling frequency, while the PC mechanism catered to a maximum rate of 250 MHz. Such a combined approach enhanced the accuracy in analyzing the differential absorption of trace gasses and allowing for the precise determination of the vertical ozone profile. Additionally, by analyzing the intensity characteristics of the echo signals, the system also calculated the extinction coefficients of aerosol in the atmosphere. This lidar system reaches a minimum temporal resolution of 3 s, a spatial resolution of 7.5 m, and an effective measurement range of 3 km. The principal operational parameters of the system are summarized in Table 1.

2.2. Data Inversion

2.2.1. Aerosol Detection

The optical properties of aerosols were inverted with the Fernald method, and the 560 nm and 590 nm channels from the received signals in the DIAL system were chosen to detect the vertical aerosol distribution [43,44]. The inversion process involved the following equation:
β a r = P r e x p − 2 S a − S m ∫ r 0 r σ m r d r P r 0 β a r 0 + β m r 0 − 2 S a ∫ r 0 r P r e x p − 2 S a − S m ∫ r 0 r σ m r ′ d r ′ d r − β m r
In Equation (1), r represents the radial distance from the radar to the target. r0 denotes the distance of a calibration point that is typically used to validate or verify the measurement accuracy of the radar system. m and a identifies the parameters related to atmospheric molecules and aerosol. β a r and β m r are the backscatter coefficients of aerosols and atmospheric molecules at range r. σ m r is the extinction coefficient of atmospheric molecules. β m r and σ m r can be obtained from sounding data or atmospheric models [43]. The lidar ratio of aerosol S a = σ a r / β a ( r ) typically uses a hypothetical constant. According to Rayleigh scattering theory, the lidar ratio of atmospheric molecules can be determined as S m = σ m ( r ) / β m ( r ) = 8 π / 3 ; P is the received signal strength intensity.
Using Equation (1), the extinction coefficients σ 1 , σ 2 and backscattering coefficients β 1 , β 2 of aerosols at the 560 nm and 590 nm channels can be obtained.
In the following equations, we assume that the Angstrom wavelength index of aerosols follows a power-law distribution:
σ λ = σ 0 λ − υ ; β λ = β 0 λ − η ;
Here, υ and η are the aerosol extinction and backscatter wavelength dependence, respectively; σ 0 and β 0 are constants. σ 0 and β 0 are linked to the aerosol concentration, while υ and η are the wavelength exponents and are correlated with the aerosol types. Substituting σ 1 , σ 2 and β 1 , β 2 into Equation (2), the wavelength dependence υ and η can be obtained.

2.2.2. Ozone Concentration Inversion

With one pair of wavelengths (the on and off laser wavelengths) in the UV absorption band of ozone, one can derive the ozone profile with the DIAL technique as follows:
N ( z ) = − 1 2 δ λ on ( T ) − δ λ off ( T ) d d z ln ⁡ P λ on ( z ) P λ off ( z ) + N B + N E
where N B and N E represent the errors caused by aerosol backscattering and extinction, respectively, which are expressed as follows:
N B = − 1 2 δ λ on T − δ λ off T d d z ln ⁡ β λ on z β λ off z    
N E = − N ( Z ) δ λ on T − δ λ off T α λ on z − α λ off z  
where λ on and λ off represent the strong absorption channel of ozone and the weak absorption channel of ozone, δ λ T is the absorption cross-section of ozone, P λ z is the echo power, β λ z is the backscattering coefficient between aerosols and atmospheric molecules, and α λ z is the extinction coefficient of aerosols and atmospheric molecules. To accurately retrieve ozone concentrations, it is necessary to determine the optical properties of aerosols, especially to determine the terms N B and N E in Equations (4) and (5). However, dual-channel ozone differential absorption lidar (DIAL) systems, which typically operate at 280 nm and 295 nm, are significantly affected by ozone absorption. Because these wavelengths lie within the ozone absorption band, the signal strength is greatly influenced by atmospheric ozone absorption, rendering these signals unsuitable for retrieving aerosol optical properties. This impact renders the signals unsuitable for retrieving the optical properties of aerosol, thus precluding the calculation of the error terms N B and N E . Consequently, these terms are often neglected in ozone concentration inversions, and the results are directly derived from the first term on the right-hand side of Equation (3).
The relationship between the optical properties of aerosol at different wavelengths can be described by Equation (2). When the atmospheric aerosol is low, lower values of σ 0 and β 0 are yielded, and the aerosol wavelength exponent is approximately 1—particularly when the wavelengths for λ on and λ off are close. The values of ln ⁡ β λ on z β λ off z and α λ on z − α λ off z in Equations (4) and (5) approach zero.
However, the aerosol wavelength exponent exhibits considerable variability under conditions of high aerosol content, such as sandstorms or intense haze. The discrepancies in the optical properties of aerosol across different wavelengths become pronounced, which results in substantial values for ln ⁡ β λ on z β λ off z and α λ on z − α λ off z in Equations (4) and (5). Neglecting such aerosol-associated terms N B and N E in ozone concentration inversion can lead to significant inaccuracies.
Thus, to accurately determine ozone concentrations, it is crucial to ascertain the optical properties of aerosol along the detection pathway. In this study, the 280 nm and 295 nm channels were applied for differential detection of ozone, while the wavelengths of 560 nm and 590 nm were minimally influenced by ozone and therefore are applicable for aerosol detection. Assuming a power-law distribution of aerosols, the wavelength indices υ and η are computed using Equation (2), facilitating the determination of the optical properties of aerosol ( α λ on z , α λ off z , β λ on z and β λ off z ) at the 280 nm and 295 nm wavelengths. This accurate specification of the aerosol-related terms N B and N E in Equation (3) enabled a more precise inversion of the ozone concentration, which is denoted as N(z).

2.3. Ground-Level Observation System

The data on air quality components near the ground in this study were released by national monitoring stations (https://www.aqistudy.cn/historydata (accessed on 1 December 2023), Meteorological element data from the National Climatic Data Center (NCDC), a division of the National Oceanic and Atmospheric Administration (NOAA). The data were accessed through NCDC’s publicly available FTP server (ftp://ftp.ncdc.noaa.gov/pub/data/noaa/isd-lite, accessed on 1 December 2023).

3. Results

3.1. Analysis of the Impact of Aerosol

When the concentration of aerosols in the atmosphere is high, it significantly impacts the accuracy of ozone concentration profile inversion using ozone lidar. A four-wavelength ozone lidar was used to observe aerosols and ozone during a sandstorm from 15 to 19 April 2021. The observation point was located near the Ecological Environment Bureau of Anyang City, Henan Province, China (114.5°E, 36°N). This study analyzed the impact of aerosols on the accuracy of ozone concentration inversion in an atmosphere with a high aerosol content. The ozone lidar detection system used lasers with wavelengths of 560 nm and 590 nm to detect aerosols. After inverting the aerosol extinction coefficients and backscatter coefficient of these two bands using Equation (1), the average values of the wavelength indices for 560 nm and 590 nm were calculated as υ = 1.534 and η = 1.742, respectively, using Equation (2).
The ozone lidar detection system detected ozone at two wavelengths, 280 nm and 295 nm. Based on the calculated wavelength index and the backscatter coefficients υ and η of the aerosol extinction coefficients at 560 nm and 595 nm obtained from the lidar inversion, the optical properties of the aerosol at the wavelengths of 280 nm and 295 nm could be calculated using Equation (2) again. The extinction and backscatter coefficients of the aerosol at 280 nm are shown in Figure 2. It can be seen that during this sandstorm, there were high concentrations of aerosols near the ground and aerosol transport in the air from 1 km to 2.5 km.
The ozone absorption cross-sections at 280 nm and 295 nm are δ λ 280 = 2.15 × 10−18 and δ λ 295 = 4.3 × 10−19, respectively [44]. According to Equations (4) and (5), the interference terms N B and N E of atmospheric aerosols could be calculated. By placing them in Equation (3), the ozone concentration without the influence of atmospheric aerosols was obtained, as shown in Figure 3. It was found that the vertical distribution of ozone in the Anyang area was mainly concentrated within a range of 1.5 km above ground level during this process. In the early morning of 15 April, a low-value zone of ozone concentration appeared near a height of 300 m above ground level, which may have been due to the “titration effect” near the ground level before sunrise in a stable atmospheric environment.
Considering the influence of atmospheric aerosols on the inversion of ozone concentration profiles, the ozone concentration inversion errors caused by the extinction and backscattering of atmospheric aerosols can be represented as − N E / N and − N B / N , respectively. The error distribution of this dust process is shown in Figure 4. It could be seen that the impact of backscattering on ozone detection was relatively small and can be ignored. The extinction coefficient had a significant impact on ozone detection, with a maximum of 150%. If the influence of the extinction coefficient is ignored, it will cause significant errors.
Three profiles were picked to analyze the impact of error in the dust process on ozone concentration inversion, namely before the dust (14:00 on 14 April), during the dust (7:00 on 15 April), and after the dust (13:00 on 17 April), as shown in Figure 5. It can be seen that the aerosol extinction coefficient climbed up to 1 km−1–1.2 km−1 due to the high aerosol concentration near the ground, resulting in an ozone concentration inversion error of about 10% during the pre-dust and post-dust periods. Over the height of 500 m, the aerosol concentration was slightly lower, and the aerosol extinction coefficient was below 0.5 km−1. It showed that when the height decreased, the ozone concentration inversion error decreased to less than 5%. During sand periods, the aerosol concentration was much higher than those before and after sand periods, with extinction coefficients approaching 2.5 km−1, resulting in an ozone inversion error of more than 10% below 1.5 km. At this time point, the highest inversion error of the ozone concentration near the ground was close to 90%. The result also illustrated that the errors caused by the extinction term in the dust process were always positive. Therefore, the four-wavelength ozone lidar used in this study was able to significantly improve the accuracy of ozone detection results due to its ability to invert the wavelength index information of aerosols.
To evaluate the impact of algorithm optimization on ozone concentration profiles, Figure 6 clearly demonstrates the significant influence of sand dust weather on ozone concentration profiles and the efficiency of the algorithm under such conditions. During sand dust weather events (07:00 on 15 April 2021), the optimization effect of the algorithm was particularly pronounced, with an average optimization ratio of 19% and a peak value reaching 63%. This indicates the algorithm’s strong adaptability and optimization capability in complex dust environments. In contrast, the optimization ratios before (14:00 on 14 April 2021) and after (13:00 on 17 April 2021) the sand dust weather remained at lower and more stable levels (averaging around 7%), with a maximum optimization ratio of approximately 22%. Figure 7 visually compares the ozone concentration profiles at three different time points, further corroborating the significant differences in optimization effects.
This study compared ground-level ozone concentration measurements with those at 200 m obtained from the ozone lidar, highlighting the precision of the enhanced ozone lidar profile inversion algorithm. Figure 8 demonstrated that aerosols could lead to an overestimation of ozone concentration at a height of 200 m, thereby affecting the accuracy of these measurements [45]. In Figure 9, we validated the improvement in ozone concentration retrieval by examining its correlation with ground-level ozone concentration, both with (“After”) and without (“Before”) the optimization effect incorporated into the algorithm. Figure 9 showed that during the period from 14 to 17 April 2021, the optimized algorithm significantly improved the correlation between the ozone concentration at 200 m and the ground-level ozone concentration, with the overall correlation coefficient increasing from 0.74 to 0.85. The most significant improvement occurred during the dust storm event on 16 April, with the correlation increasing from 0.69 to 0.79. This indicated that the algorithm’s optimization effect was more pronounced under dust storm conditions. Therefore, future research should further explore the specific mechanisms by which dust storm conditions affected ozone concentration profiles, considering factors such as relative humidity and temperature. Enhancing the algorithm to adapt to various weather conditions would improve the accuracy and reliability of air quality monitoring, providing robust support for environmental protection and public health.

3.2. Analysis of Ozone Pollution Process

A severe ozone pollution event was reported from 26 to 30 May 2021 (Table 2). On 26 May, the air quality index (AQI) in Anyang was 81, indicating good air quality, with the maximum 8 h ozone concentration reaching 137 μg/m3. Over the following days, the ozone concentrations steadily increased. By 27 May, the AQI rose to 108, indicating mild pollution, with a maximum 8 h ozone concentration of 167 μg/m3 and a peak hourly concentration of 184 μg/m3. The pollution peaked on 29 May, with an AQI of 151, which was classified as moderate pollution. The maximum 8 h ozone concentration for that day soared to 216 μg/m3, with hourly concentrations exceeding the standard for eight hours and reaching a peak of 238 μg/m3.
Meteorological conditions, including wind direction and speed, played a significant role in the variability of surface ozone concentrations. Starting on 26 May, the prevailing north winds had an average speed of no more than 3 m/s, with daily maximum temperatures ranging from 26.3 °C to 37.8 °C. These conditions, which were indicative of poor atmospheric dispersion, facilitated the generation and accumulation of ozone. On the night of 29 May, a strong local convective process improved the diffusion conditions due to the convergence of cold and warm air masses in the boundary layer, resulting in decreased ground-level ozone concentrations. By 30 May, the air quality had significantly improved, marking the end of the pollution event.
The deployed four-wavelength ozone lidar system successfully mapped the vertical distribution of ozone from 0.3 km to 3.0 km by utilizing differential absorption and scattering across various wavelengths, as shown in Figure 10. On the morning of 27 May, increased boundary-layer turbulence led to the mixing of ozone across different layers, forming a high-ozone-pollution zone from near the ground up to 1.5 km. This zone reached its peak on the afternoon of 29 May. By 21:00 that evening, the atmospheric diffusion conditions had significantly improved due to the influence of cold air, substantially reducing ozone concentrations and concluding the pollution episode. Figure 11 illustrates the time series of ozone concentrations at various altitudes, revealing distinct diurnal patterns at ground level and at 0.3 km. The ground-level ozone concentrations peaked around 18:00 due to photochemical reactions and decreased after 21:00. Higher altitudes maintained elevated ozone levels overnight, potentially contributing to the increased ground-level concentrations the next day due to atmospheric turbulence.
Figure 12 shows the vertical ozone profiles at key times during the day of pollution on 28 May. At 02:00, 08:00, and 14:00, the ozone profiles were relatively uniform from near the ground up to 1.5 km, with slight vertical gradient variations. During these times, the overall ozone concentration gradually increased, stabilizing at about 1.5 km. Remarkably, at 08:00 during the ozone suppression stage, the concentrations near the ground and at lower altitudes (0.3 km to 1.2 km) were significantly higher than those at night. This increase was partially due to reduced vehicular emissions and a weak nitric oxide titration effect during the COVID-19 pandemic control period, allowing photochemically generated ozone to accumulate. By 14:00, the combination of rising near-ground ozone and photochemical generation at higher altitudes led to ozone with high ozone concentrations extending from the ground upward. At 20:00, the ozone concentrations decreased with altitude from near the ground up to 1.0 km, with concentrations ranging from 82 μg/m3 to 123 μg/m3. Above this layer, from 1.0 to 1.5 km, concentrations increased with altitude and then rapidly decreased above 1.5 km, stabilizing thereafter. This pattern highlighted the presence of a significant high-ozone zone near the height of the boundary layer, approximately one kilometer thick, on the day of heavy ozone pollution.

4. Discussion

This study aimed to investigate the impact of aerosols on the inversion of ozone concentration profiles and the characteristics of combined ozone and dust pollution during the spring–summer transition. Using a four-wavelength ozone lidar system, detailed observations and analyses of aerosols and ozone concentrations were conducted during a dust storm. The results revealed that high aerosol concentrations significantly affected the accuracy of ozone concentration inversions. Additionally, the role of meteorological conditions in ozone concentration changes was explored by analyzing an ozone pollution event in May 2021. These studies provide important insights for understanding and managing atmospheric pollution.

4.1. Impact of Aerosols on Ozone Concentration Profile Inversion

This study demonstrated that high aerosol concentrations in the atmosphere significantly affected the accuracy of ozone concentration profile inversions when using ozone lidar [46,47]. During a dust storm from 15 to 19 April 2021, a four-wavelength ozone lidar system was employed to observe aerosols and ozone concentrations. The impact of aerosols on the accuracy of ozone concentration inversions in a high-aerosol-content atmosphere was analyzed. The system used signals at wavelengths of 560 nm and 590 nm to invert the aerosol properties, obtaining the extinction and backscatter coefficients, and then calculated the optical properties of aerosol at different wavelengths.
The results indicated that the aerosol extinction coefficient had a substantial impact on ozone concentration inversions, with a maximum error of up to 150%. During the dust storm, the inversion error of ozone concentrations below 1.5 km exceeded 10%, with a maximum error near the ground approaching 90%. Ignoring the extinction effect of aerosols would cause ozone concentrations to be significantly overestimated. Therefore, incorporating aerosol information into the lidar inversion process is crucial for improving the accuracy of ozone monitoring. This finding aligns with previous research, which also emphasized the necessity of considering the optical properties of aerosol in atmospheric remote sensing.

4.2. Ozone Pollution Process

The ozone pollution event from 26 to 30 May 2021 provided valuable insights into surface ozone concentrations and their meteorological influences. On 29 May, the pollution event peaked, with an AQI of 151 and a maximum 8 h ozone concentration of 216 μg/m3, highlighting the impact of poor atmospheric dispersion conditions on ozone accumulation. Low average wind speeds from the north and high temperatures led to sustained increases in ozone concentrations. On 30 May, strong local convection significantly improved the air quality, indicating the importance of meteorological conditions in ozone dispersion.
Vertical distribution analysis showed that the ozone concentrations were the highest near the ground and within the boundary layer, peaking around 18:00 due to photochemical reactions [48]. This diurnal pattern was consistent with other studies’ reports on ozone behavior. The vertical profile on 28 May further illustrated the influence of boundary-layer dynamics on ozone distribution, with significant high-ozone areas appearing within the boundary-layer height.
The results of this study are important for understanding and managing ozone pollution. Effective strategies for reducing ozone pollution should consider emission control measures and meteorological conditions [20]. Future research should explore the interactions between different atmospheric layers and their contributions to surface ozone concentrations.

5. Conclusions

This study used a four-wavelength ozone lidar system to quantify the impact of aerosols on ozone concentration profile inversions and the characteristics of ozone and dust pollution during the spring summer transition. The impact of high aerosol concentrations significantly affected the accuracy of ozone concentration inversions. During dust storms in Anyang, Henan, China, the aerosol extinction coefficient reached up to 2.5 km−1, leading to ozone concentration measurement errors of up to 150%. This indicated that the optical properties of aerosol must be considered in atmospheric remote sensing to improve the ozone monitoring accuracy. Specifically, high aerosol concentrations caused ozone concentration inversion errors to exceed 10% below 1.5 km, with the maximum errors near the ground approaching 90%. The optimized algorithm significantly improved the correlation between ozone concentrations at 200 m and ground level, particularly during the dust storm event on 16 April 2021, with the correlation coefficient increasing from 0.69 to 0.79. This highlights the importance of considering aerosol optical properties in atmospheric remote sensing to enhance ozone monitoring accuracy. Analysis of an ozone pollution event from 26 to 30 May 2021 revealed that meteorological conditions significantly affected ozone concentration changes. During this period, high temperatures and low wind speeds led to sustained increases in ozone concentrations, reaching up to 238 μg/m3. After strong convective events, the air quality significantly improved, indicating the critical role of meteorological conditions in the dispersion of ozone pollution. The four-wavelength ozone lidar system effectively inverted the ozone and aerosol profiles. The application of aerosol correction algorithms combined with aerosol wavelength index information significantly improved the accuracy of ozone detection. This method notably reduced the ozone concentration inversion errors at high aerosol concentrations. Vertical distribution analysis showed that ozone concentrations were the highest near the ground and within the boundary layer, peaking around 18:00 due to photochemical reactions. The study also found a significant high-ozone area near one kilometer in height. High-ozone areas were particularly evident between 1.0 and 1.5 km, highlighting the influence of boundary-layer dynamics on ozone distribution.
In summary, this study provides quantitative data and important insights for understanding and managing atmospheric pollution. Effective ozone pollution control strategies should consider both emission control measures and meteorological conditions. Future research should further quantify the interactions between different atmospheric layers and their impact on surface ozone concentrations to develop more comprehensive air quality management strategies.

Author Contributions

The contributions of the authors to the manuscript are as follows: conceptualization, N.M., S.Y. and C.P.; methodology, N.M.; software, T.Z., S.Y., J.W. (Jianing Wan) and Y.X.; validation, J.W. (Jie Wang), T.Z. and Y.Z.; formal analysis, N.M.; resources, J.W. (Jie Wang) and T.Z.; data curation, N.M., J.W. (Jie Wang), T.Z., Y.Z. and S.Y.; original draft preparation, N.M. and J.W. (Jie Wang); writing—review and editing, N.M. and J.W. (Jie Wang); visualization, S.Y., J.W. (Jianing Wan); supervision, J.W. (Jie Wang) and C.P.; project administration, N.M. and J.W. (Jie Wang), funding acquisition, J.W. (Jie Wang) and Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the research project on pollution gas emission observation and quality control technology (2024KYHXXM001), National Key Research and Development Program of China (2022YFC3700105, 2022YFC3700100), Study on Gaseous Air Pollutants Offshore Southeast China (AS 22026C, AHU-HK-202310) and Strategic Research and Consulting Project of the Chinese Academy of Engineering (2022-06-04).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

We thank Wenqing Liu, Meng Yang, and Zhongwen Bao for their guidance on experiments and writing strategies. We also thank the China National Environmental Monitoring Centre (CNEMC) for the AQI data.

Conflicts of Interest

Author Jianing Wan was employed by the company Wuxi Cas Photonics Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

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Figure 1. Schematic diagram of the four-wavelength ozone lidar system.
Figure 1. Schematic diagram of the four-wavelength ozone lidar system.
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Figure 2. Optical properties of atmospheric aerosols: (a) profile of the atmospheric aerosol backscatter coefficient; (b) profile of the atmospheric aerosol extinction coefficient.
Figure 2. Optical properties of atmospheric aerosols: (a) profile of the atmospheric aerosol backscatter coefficient; (b) profile of the atmospheric aerosol extinction coefficient.
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Figure 3. Ozone concentration profile.
Figure 3. Ozone concentration profile.
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Figure 4. Errors analysis caused by high-concentration aerosol: (a) the error distribution of backscatter coefficients; (b) the error distribution of extinction coefficients.
Figure 4. Errors analysis caused by high-concentration aerosol: (a) the error distribution of backscatter coefficients; (b) the error distribution of extinction coefficients.
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Figure 5. (a) Error caused by the extinction term; (b) aerosol extinction coefficient.
Figure 5. (a) Error caused by the extinction term; (b) aerosol extinction coefficient.
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Figure 6. Concentration of ozone optimized with (After) and without (Before) aerosol consideration. (a) At 14:00 on 14 April 2021; (b) 07:00 on 15 April 2021; (c) 13:00 on 17 April 2021.
Figure 6. Concentration of ozone optimized with (After) and without (Before) aerosol consideration. (a) At 14:00 on 14 April 2021; (b) 07:00 on 15 April 2021; (c) 13:00 on 17 April 2021.
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Figure 7. Improvement rate of ozone concentration profiles before (black), during (red), and after (green) sand dust event.
Figure 7. Improvement rate of ozone concentration profiles before (black), during (red), and after (green) sand dust event.
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Figure 8. Time series of ozone concentration at 200 m retrieved with (red) and without (black) considering aerosol and ground level (blue).
Figure 8. Time series of ozone concentration at 200 m retrieved with (red) and without (black) considering aerosol and ground level (blue).
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Figure 9. The correlation coefficients of between ground level and with (red) and without (black) considering aerosol.
Figure 9. The correlation coefficients of between ground level and with (red) and without (black) considering aerosol.
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Figure 10. Vertical distribution of ozone observed using ozone lidar from 26 to 30 May.
Figure 10. Vertical distribution of ozone observed using ozone lidar from 26 to 30 May.
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Figure 11. Comparison of the monitoring results between a ground -level automatic ozone analyzer and ozone lidar at different altitudes.
Figure 11. Comparison of the monitoring results between a ground -level automatic ozone analyzer and ozone lidar at different altitudes.
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Figure 12. Vertical profiles of ozone concentrations at different times on 28 May.
Figure 12. Vertical profiles of ozone concentrations at different times on 28 May.
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Table 1. The technical parameters of the four-wavelength ozone lidar system.
Table 1. The technical parameters of the four-wavelength ozone lidar system.
Emitting SystemReceiving System Data Acquisition and Control System
Laser typeNd:YAGTelescope typeCassegrain reflection typeDetector typePhotomultiplier tube
Laser wavelength/nm532Telescope aperture/mm200Sampling digit of acquisition card/Bit12-bit
Detection wavelength/nm560, 590, 280, and 295Beam expansion divergence angle/rad0.2×10−3Sampling frequency of acquisition card/ MHz40
Pulse frequency/Hz100Receiving field of view angle/rad1.5 × 10−3Photon counting/MHz250
Single-pulse energy/mJ30Spectral methodFilter
Raman tubeSolid-state Raman tube
Table 2. Air quality and meteorological data from 26 to 30 May 2021.
Table 2. Air quality and meteorological data from 26 to 30 May 2021.
DateAQIMaximum 8 h Mass Concentration
ρ (μg·m−3)		Maximum Hourly Mass Concentration
ρ (μg·m−3)		Average Wind Speed
v (m·s−1)		Dominant Wind DirectionDaily Maximum Temperature
θ (°C)
26 May 2021811371841.8N33.2
27 May 20211081671842.9NW37.8
28 May 20211331561772.5N31.2
29 May 20211512162383.0NNE37.2
30 May 2021981571631.6N26.3
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Ma, N.; Wang, J.; Pei, C.; Yang, S.; Zhang, T.; Zhang, Y.; Wan, J.; Xu, Y. The Accurate Inversion of the Vertical Ozone Profile in High-Concentration Aerosols Based on a New DIAL-A Case Study. Remote Sens. 2024, 16, 2997. https://doi.org/10.3390/rs16162997

AMA Style

Ma N, Wang J, Pei C, Yang S, Zhang T, Zhang Y, Wan J, Xu Y. The Accurate Inversion of the Vertical Ozone Profile in High-Concentration Aerosols Based on a New DIAL-A Case Study. Remote Sensing. 2024; 16(16):2997. https://doi.org/10.3390/rs16162997

Chicago/Turabian Style

Ma, Na, Jie Wang, Chenglei Pei, Sipeng Yang, Tianshu Zhang, Yujun Zhang, Jianing Wan, and Yiwei Xu. 2024. "The Accurate Inversion of the Vertical Ozone Profile in High-Concentration Aerosols Based on a New DIAL-A Case Study" Remote Sensing 16, no. 16: 2997. https://doi.org/10.3390/rs16162997

APA Style

Ma, N., Wang, J., Pei, C., Yang, S., Zhang, T., Zhang, Y., Wan, J., & Xu, Y. (2024). The Accurate Inversion of the Vertical Ozone Profile in High-Concentration Aerosols Based on a New DIAL-A Case Study. Remote Sensing, 16(16), 2997. https://doi.org/10.3390/rs16162997

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