Local Emission Sources Research Articles

Characteristics, source, and health risk assessment of aerosol polyaromatic hydrocarbons in the rural and urban regions of western Saudi Arabia

Abstract Air quality represents one of the most important parameters determining indoor microclimate and human comfort. Thus, the current study reports a comprehensive study on the dominant sources, organic compositions, and potential health impacts of the polyaromatic hydrocarbons (PAHs) in the atmospheric particle matters (PMs) ranging from 2.5 µm (PM2.5) to 10 µm (PM10) size in the rural and urban regions of western (Jeddah city) Saudi Arabia collected over 1 year between 2014 and 2015. The levels of PAHs in two locations namely Obhur (Urban) and Hada Alsham (Rural) were monitored over 1 year (2014 and 2015) using the gas chromatography coupled mass spectrometry. The level of ƩPAHs in Obhur (819.25 ng/m3) has a significantly high concentration of PAHs compared to Hada Alsham (Rural) (675.26 ng/m3). Indeno(1,2,3-CD)pyrene was the major contributor with an average value of 215.66 ng/m3 followed by benzo[k]fluranthene with a concentration of 150.68 ng/m3, respectively. The major contributors were indeno[1,2,3-cd]pyrene, benzo[k]fluranthene, dibenzo[a,h]anthracene, benzo[g,h]perylene, and benzo[b]fluranthene are the major contributors with contributing percentages of 26.32, 18.39, 9.07, and 8.29%, respectively. The rest of all compounds were below 4%. The highest concentrations of PAHs in Obhur (1836.99 ng/m3) and in Hada Alsham (1107.40 ng/m3) were observed in winter in January 2014. PAHs with 4–6 aromatic ring components are primarily emitted by high temperature combustion. The average values for the BaA/(BaA + Chr) and Flt/(Flt + Pyr) ratios at Obhur were found 0.58 and 0.43 and at Hada Alsham were found 0.63 and 0.38, respectively, indicating that coal/biomass burning is the major source of PAHs. Hada Alsham (rural area), the transportation system, is a significant contributor to the observed PAHs. These results reflect Saudi Arabia’s traffic load in both rural and urban areas. On road sites, the impact of petroleum combustion and vehicular emissions was also identified. The sum of the incremental lifetime cancer risk (ILCR) for all congeners for infants along the Obhur location was 2.13 × 10−6 and 1.38 × 10−6, respectively. ILCR values were less than 1.0 × 10−4, implying that PAH exposure posed an acceptable potential cancer risk in this study. Various local emission sources contributed more PAHs in many Saudi urban areas, increasing the risk of lung cancer, and the health risk. PAHs have an associated large surface area and are capable of deposition in the respiratory system with high efficiency. The total health risk assessment study also helps in alarming the toxicity at both the locations.

Characteristics, source, and health risk assessment of aerosol polyaromatic hydrocarbons in the rural and urban regions of western Saudi Arabia

Abstract Air quality represents one of the most important parameters determining indoor microclimate and human comfort. Thus, the current study reports a comprehensive study on the dominant sources, organic compositions, and potential health impacts of the polyaromatic hydrocarbons (PAHs) in the atmospheric particle matters (PMs) ranging from 2.5 µm (PM2.5) to 10 µm (PM10) size in the rural and urban regions of western (Jeddah city) Saudi Arabia collected over 1 year between 2014 and 2015. The levels of PAHs in two locations namely Obhur (Urban) and Hada Alsham (Rural) were monitored over 1 year (2014 and 2015) using the gas chromatography coupled mass spectrometry. The level of ƩPAHs in Obhur (819.25 ng/m3) has a significantly high concentration of PAHs compared to Hada Alsham (Rural) (675.26 ng/m3). Indeno(1,2,3-CD)pyrene was the major contributor with an average value of 215.66 ng/m3 followed by benzo[k]fluranthene with a concentration of 150.68 ng/m3, respectively. The major contributors were indeno[1,2,3-cd]pyrene, benzo[k]fluranthene, dibenzo[a,h]anthracene, benzo[g,h]perylene, and benzo[b]fluranthene are the major contributors with contributing percentages of 26.32, 18.39, 9.07, and 8.29%, respectively. The rest of all compounds were below 4%. The highest concentrations of PAHs in Obhur (1836.99 ng/m3) and in Hada Alsham (1107.40 ng/m3) were observed in winter in January 2014. PAHs with 4–6 aromatic ring components are primarily emitted by high temperature combustion. The average values for the BaA/(BaA + Chr) and Flt/(Flt + Pyr) ratios at Obhur were found 0.58 and 0.43 and at Hada Alsham were found 0.63 and 0.38, respectively, indicating that coal/biomass burning is the major source of PAHs. Hada Alsham (rural area), the transportation system, is a significant contributor to the observed PAHs. These results reflect Saudi Arabia’s traffic load in both rural and urban areas. On road sites, the impact of petroleum combustion and vehicular emissions was also identified. The sum of the incremental lifetime cancer risk (ILCR) for all congeners for infants along the Obhur location was 2.13 × 10−6 and 1.38 × 10−6, respectively. ILCR values were less than 1.0 × 10−4, implying that PAH exposure posed an acceptable potential cancer risk in this study. Various local emission sources contributed more PAHs in many Saudi urban areas, increasing the risk of lung cancer, and the health risk. PAHs have an associated large surface area and are capable of deposition in the respiratory system with high efficiency. The total health risk assessment study also helps in alarming the toxicity at both the locations.

Using Ground- and Drone-Based Surface Emission Monitoring (SEM) Data to Locate and Infer Landfill Methane Emissions

Ground- and drone-based surface emission monitoring (SEM) campaigns were performed at two municipal solid waste landfills, during the same week as mobile tracer correlation method (TCM) testing was used to measure the total methane emissions from the same landfills. The G-SEM and the D-SEM data, along with wind data, were used as input into an inverse modeling approach combined with an optimization-based methane emission estimation method (implemented in a tool called SEM2Flux). This approach involves the use of backward dispersion modeling to estimate the whole-site methane emissions from a given landfill and the identification of locations and emission rates of major leaks. SEM2Flux is designed to exploit the measured surface methane concentration concurrently with wind data and tackle two problems: (1) inferring the estimates of methane rates from individual landfills, and (2) identifying the likely locations of the main emission sources. SEM2Flux results were also compared with emission estimates obtained using TCM. In Landfill B, the average TCM-measured methane emissions was 1178 Kg/h, with a standard deviation of 271 Kg/h. In Landfill C, the average TCM-measured emission rate was 601 Kg/h, with a standard deviation of 292 Kg/h. For both landfills, the D-SEM data yielded statistically similar estimates of methane emissions as the TCM-measured emissions. On the other hand, the G-SEM data yielded comparable estimates of emissions to TCM-measured emissions only for Landfill C, where the D-SEM and G-SEM data were statistically not different. The results of this study showcase the ability of this method using surface concentrations to provide a rapid and simple estimation of fugitive methane emissions from landfills. Such an approach can also be used to assess the effectiveness of different remedial actions in reducing fugitive methane emissions from a given landfill.

Characteristics, sources, and health risk assessment of atmospheric particulate mercury in Guanzhong Basin

Mercury (Hg) has received increasing public attention owing to its high toxicity and global distribution capability via long-range atmospheric transportation. Guanzhong Basin (GB) is vital for the industrial and economic development of Shaanxi Province. To determine the concentration, spatial distribution, seasonal variation, sources, and health risks of particulate-bound mercury (PBM), PM2.5 samples were collected at three sampling sites representing urban, rural, and remote areas during winter and summer in GB. The three sampling sites were in Xi'an (XN), Taibai (TB), and the Qinling Mountains (QL). The mean PBM concentrations in XN, TB, and QL in winter were 130 ± 115 pg m−3, 57.5 ± 47.3 pg m−3, and 53.6 ± 38.5 pg m−3, respectively, higher than in summer (13.7 ± 7.11 pg m−3, 8.01 ± 2.86 pg m−3, and 7.75 ± 2.85 pg m−3, respectively). PBM concentrations are affected by precipitation, meteorological conditions (temperature and mixed boundary layer), emission sources, and atmospheric transport. During the sampling period, the PBM dry deposition in XN, TB, and QL was 1.90 μg m−2 (2 months), 0.835 μg m−2 (2 months), and 0.787 μg m−2 (2 months), respectively, lower than the range reported in national megacities. According to backward trajectory and potential source contribution factor (PSCF) analysis, mercury pollution in XN is mainly affected by local pollution source emissions, whereas the polluted air mass in TB and QL originates from local anthropogenic emissions and long-distance atmospheric transmission. The non-carcinogenic health risk values of PBM in XN, TB, and QL in winter and summer were less than 1, indicating that the risk of atmospheric PBM to the health of the residents was negligible.

Error distribution and influencing factors of acoustic emission source location for sensor rectangular network

The arrangement of acoustic emission (AE) sensors would influence the location accuracy. This study focuses on two-dimensional source location using four sensors, which are placed at the four corners of a rectangle. To assess the influence of sensor arrangement, numerous simulation experiments were conducted with different aspect ratios of the rectangle. The results reveal that there is a highly accurate location area at the central region of the rectangle and four regions with pronounced location errors along the symmetry axis. This means that sensor arrangement should be strategic to make the interested monitoring area not at the above range. Additionally, as the aspect ratio increases, the location accuracy decreases. When the aspect ratio surpasses 7:1, the location results would be seriously distorted. Furthermore, the observed error distribution pattern is validated through pencil-lead breaking experiments and the corresponding explanation is provided to clarify these findings.

The role of large-scale atmospheric circulations on long-term variations of PM10 concentrations over Turkey.

PM10 is widely identified as an important atmospheric pollutant posing a serious threat to human health and environment as well as it influences the climate system. To unearth the mechanism involved in its sources and circulation behavior in environment, this study focuses on the role of large-scale atmospheric circulation on the long-term variability of PM10 over Turkey by applying rotated empirical orthogonal functions (REOF) analysis. As a result of the implementation of REOF to the daily PM10 data for 80 air quality stations throughout the period 2010-2020, first REOF mode (REOF1 44.9% in winter, 43.2% in spring, 39.5% in summer and 31.6% in fall) for all the four seasons indicated the role of local emission sources on the variations of PM10, which show high PM10 values in different geographical regions. The results of the second mode (REOF2, 17.9% in winter, 14.0% in spring, 14.0% in summer and 16.3% in fall) indicate the role of large-scale atmospheric circulations on the values of PM10. From the REOF2 analysis and extracted synoptic composite maps, the strength of southerly winds and the presence of southwesterly winds at low levels are very important in transporting of dust pollutants from the Arabian Peninsula and Northern Africa, respectively, to the eastern (EAR) and southeastern (SEAR) regions of Turkey during winter. In spring, sand particles in the interior terrestrial part of the country are carried to the northern regions by the effect of large-scale southerly winds, which cause above-normal PM10 concentrations in the Black Sea region of Turkey. In summer, dust particles together with warm dry air intrusion to the eastern region of Turkey by strong easterly winds are sourced by Caspian Sea and result in high PM10 values. Our findings emphasize that the long-term variations in air quality over Turkey are affected secondary by the variations in the large-scale atmospheric circulations with primary contributions from the changes in local emission sources.

Numerical Modeling of Localization of an Acoustic Emission Source in a Solid Using Distributed Fiber-Optic Sensors

Numerical Modeling of Localization of an Acoustic Emission Source in a Solid Using Distributed Fiber-Optic Sensors

Deep residual learning for acoustic emission source localization in A steel-concrete composite slab

Large errors can be introduced in traditional acoustic emission (AE) source localization methods using extracted signal features such as arrival time difference. This issue is obvious in the case of irregular structural geometries, complex composite structure types or presence of cracks in wave travel paths. In this study, based on a novel deep learning algorithm called deep residual network (DRN), a structural health monitoring (SHM) strategy is proposed for AE source localization through classifying and recognizing the AE signals generated in different sub-regions of critical areas in structures. Hammer hits and pencil-leak break (PLB) tests were carried out on a steel-concrete composite slab specimen to register time-domain AE signals under multiple structural damage conditions. The obtained time-domain AE signals were then converted into time-frequency images as inputs for the proposed DRN architecture using the continuous wavelet transform (CWT). The DRNs were trained, validated and tested by AE signals generated from different source types at various damage states of the slab specimen. The proposed DRN architecture shows an effective potential for AE source localization. The results show that the DRN models pre-trained by the AE signals obtained in the undamaged specimen are able to accurately classify and identify the locations of different types of AE sources with 3–4.5 cm intervals even when multiple cracks with widths up to 4–6 mm are present in the wave travel paths. Moreover, the influence factors on the model performance are investigated, including structural damage conditions, sensor-to-source distances and AE sensor mounting positions; in accordance with the parametric analyses, recommendations are proposed for the engineering application of the proposed SHM strategy.

Study on Joint Method of 3D Acoustic Emission Source Localization Simplex and Grid Search Scanning

Study on Joint Method of 3D Acoustic Emission Source Localization Simplex and Grid Search Scanning

Wet deposition of sulfur and nitrogen in the Jiuzhaigou World Heritage Site, China: Spatial variations, 2010–2022 trends, and implications for karst ecosystem conservation

Excessive sulfur and nitrogen deposition is a threat to natural ecosystems. This study investigated the spatio-temporal changes of wet sulfur and nitrogen deposition in Jiuzhaigou to understand the responses of wet deposition chemistry to local and national air pollutant emission changes and to assess relevant ecological risks. The results show that local emission sources (e.g., tourism, post-earthquake reconstruction, and vegetation damages) largely elevated the concentrations of SO42−, NO3−, NH4+, and Ca2+ at the non-background sites during 2021–2022; However, the gaps of annual sulfur and nitrogen wet deposition fluxes among the background, semi-background, and non-background sites were reduced by annual precipitation. The pH was also higher at the non-background sites due to stronger acid neutralization capacities from crustal dust there, and acid precipitation (pH < 5.6) was not detected at all the sites. During the 2010 to 2022 period, the wet deposition of SO42− and TIN (i.e. total inorganic nitrogen) and the equivalent ratio of SO42−: NO3− generally presented a declining trend at the background and semi-background sites, as a result of regional and national emission reductions. The TIN reduction from 2015 to 2016 to 2021–2022 at the background site was more from wet NH4+ deposition and the site's NO3− concentrations and fluxes were relatively stable. Compared to the critical loads (CLs) of sulfur and nitrogen for acidification and eutrophication, the annual wet and wet+dry deposition fluxes of sulfur and nitrogen were lower in almost the entire watershed, except for the CLmin of nitrogen for acidification in considerable areas. However, all the wet deposition samples were calcite-unsaturated and had annual TIN concentrations higher than the national environmental standard of total nitrogen for surface water. More field investigations are needed to better assess the ecological risks.

Two-dimensional acoustic emission source localization on layered engineered wood by machine learning: a case study of laminated veneer lumber plate structure

Engineered wood or mass timber has gained increasing popularity in building construction, and layered engineered wood is a major category of mass timber design since it enables manufacturing structural members with a wide range of geometry. Thus, there is a potential rising demand for structural health monitoring on engineered wood-based structural members and buildings. This study investigates the feasibility of using an important and practical acoustic emission (AE) method for damage localization, specifically two-dimensional (2D) AE source localization, in a representative layered engineered wood sample, namely laminated veneer lumber (LVL) plate. While 2D AE source localization is generally straightforward in isotropic materials, the problem becomes challenging for anisotropic materials with angle-dependent wave velocities. It is even more complicated if heterogeneity involves, which turns out to be the case for layered engineered wood. In this study, we rely on the AE feature of difference in time of arrival (dTOA) and develop three methods to address the challenges of 2D AE source localization raised by anisotropy and heterogeneity in an LVL plate. The benchmark velocity profile method (VPM) is first implemented in an LVL plate. With knowledge of the angle-dependent velocity, the source location predictions by the VPM are generally erroneous even with predicted source location outside of the region of interest. Furthermore, the general regression neural network (GRNN) is developed using different combinations of dTOA components, resulting in improved prediction performance. Third, the Gaussian process regression (GPR) is developed by maximizing the marginal likelihood of the training dataset. Moreover, to lessen the computation burden, the lower bound of the logarithm likelihood of the whole models is derived and decomposed through Jensen’s inequality and Bayes’ theorem, providing the theoretical background for training models with different combinations of dTOAs individually.

The influence of relative humidity and emission source location on the migration regularity of bioaerosols in a typical urban environment

The influence of relative humidity and emission source location on the migration regularity of bioaerosols in a typical urban environment

Spatial and Temporal Distribution Characteristics of Ozone Concentration and Source Analysis during the COVID-19 Lockdown Period in Shanghai

In March 2022, a new wave of COVID-19 outbreak occurred in Shanghai due to the widespread transmission of the Omicron variant. A two-month citywide lockdown was implemented from April 1st to May 31st, adopting measures such as zone-based classification and grid management. This unique social event provided an “ideal air quality experiment” for pollution research. The rapid reduction in economic activities during the lockdown had many positive impacts on the environment, leading to overall improvements in air quality. Particularly, the concentration of NOx, one of the precursors to O3, significantly decreased. However, O3, as a typical secondary pollutant, showed a noticeable increase. This study uses the WRF-CAMx-OSAT air quality model method to analyze the source of O3 pollution in Shanghai from April to May 2022. The impact of O3 precursor control, sector sources, and regional contributions on the formation of O3 pollution in Shanghai is analyzed in depth. During the pandemic lockdown period, it was found that, in Shanghai, the overall O3 levels were controlled by VOCs (Volatile Organic Compounds), and controlling VOCs proved to be an effective measure in reducing O3 concentrations in Shanghai. Compared with the same period in 2021, the proportion of road traffic sources contributing to ozone concentration has significantly decreased from 70.61% to 64.3%, but they are still the largest contributor. The contribution of industrial emissions to the ozone concentration has significantly risen from 20.71% to 26.36%, making them still the second largest contributor. Industrial and traffic sources are emission sources that require particular attention. The contribution ratio of local sources to external transport is about 7:3, which is higher than the ratio of local sources to external transport in the same period of 2021, which is about 6:4. The local ozone is the main source of ozone concentration in Shanghai, and controlling local source emissions is the key to controlling ozone concentration in the Shanghai area. When excluding the impact of long-range transport, the main areas contributing to O3 formation from local sources are Baoshan District, Jiading District, Qingpu District, and Chongming District, accounting for approximately 41.12% of the total absolute contribution. Different source regions exhibit significant spatial variations in their contributions to the ozone concentration. Through these studies, we aim to provide scientific support and control suggestions for the precise prevention and control of O3 pollution in Shanghai.

Soils are a non-negligible source of NO in a UK suburban greenspace and SE Australian Eucalyptus forest

Nitrogen oxides, particularly NO and NO2 (NOx), are primary air pollutants that also play an essential role in the atmospheric oxidation of volatile organic compounds, resulting in ozone and secondary organic aerosol formation. It is therefore critical to fully characterise NOx sources and sinks to understand tropospheric photochemistry and hence local- to regional-scale air quality. Human activities such as transport and power plants are well-known NOx emission sources in urban areas, whereas natural sources such as soils have been considered to contribute more substantially in rural and remote areas. However, soil NO emissions are poorly characterised and therefore underrepresented in models. To improve our understanding of soil as a source of NO, we measured diurnal patterns in soil NO concentrations at a suburban site in the UK and a remote field site in Australia to determine whether soils contribute to local atmospheric NO, and to identify the potential drivers of soil NO emissions. Mean soil NO concentrations in both UK campaigns 1.76 ± 0.92 ppb in summer and 0.91 ± 0.37 ppb in winter) were higher than those measured in Australia (0.73 ± 0.73 ppb). The diel patterns of NO concentrations (both sites) and emissions rates (Australia) showed a clear peak corresponding to local emission sources, but variation in NO was also related to either vapour pressure deficit (R2 = 0.88 in UK summer, R2 = 0.51 in Australia, both p < 0.05) or solar radiation (R2 = 0.06 with p > 0.4 in UK summer, R2 = 0.71 with p < 0.05 in Australia) during the daylight hours, indicating biogenic origin of soil NO. Our work demonstrates that biogenic soil emissions of NO are non-negligible, estimated at around 1.32 % of total NO emissions at the remote site, and 0.22 % at the urban site, and must be accounted for in global and regional atmospheric chemistry-climate modelling and NOx reduction strategies.

Acoustic emission source location method for glass fibre reinforced plastics based on virtual loading focusing enhancement technology

ABSTRACT Because of the inhomogeneity and anisotropy of glass fibre reinforced plastics (GFRP), it is always a challenge to detect the real-time dynamic quality during manufacturing and service. In this paper, a virtual loading focusing enhancement technology (VLFET) based on time reversal (TR) theory is proposed to accurately locate the acoustic emission (AE) source in the GFRP. First, the focusing enhancement theoretical model with virtual reverse loading is deduced according to the TR theory. Then, after measuring the optimal detection range and the average acoustic velocity, the sensor array is positioned on the GFRP specimen. The excitation signals applied to simulate the AE source at some positions are produced by using lead breaking, and the detection signals are received by the sensor array. Finally, the signals received by the sensor array are processed according to the theoretical model, and the fluctuation image in the monitoring area is reconstructed with fluctuation amplitude of each pixel to evaluate the AE source position. The experimental results demonstrate that the excitation sources can be accurately evaluated with the VLFET. The location error with the proposed method is less than 3.1%, higher than that from the current AE instrument. The proposed technology can also be applied to the AE evaluation of other anisotropic materials.

Size-resolved inhalation intake fractions for particles released from human activities in residential indoor environments

Inhalation exposure to elevated concentrations of airborne particulate matter is a public health concern. Assessment of exposure can be enhanced through better knowledge of source-receptor relationships, which can be characterized through the inhalation intake fraction metric. This case study provides new insights on variations in particle inhalation intake fractions for indoor sources associated with common human activities in residential buildings. In a controlled climate chamber (air temperature: 24 ± 1 °C, relative humidity: 50 ± 5%), we investigated size-resolved intake fractions for particles in relation to four scripted activities performed by a human volunteer: sitting, walking, cooking, and vacuuming. We measured size- and time-resolved particle number concentrations at the volunteer's breathing zone to characterize intake fractions. In addition, we measured particles at four different stationary locations across the climate chamber to assess the degree of spatial heterogeneity in particulate matter concentrations. The results show that particles released from human skin and clothing during sitting were associated with the highest total inhalation intake fraction (13‰), followed by cooking (9‰), vacuuming (5.7‰), and walking (3.9‰). These results highlight how breathing zone proximity to localized emission sources and low indoor air mixing can enhance inhalation exposure to particles. Sitting and cooking caused a maximum inhalation intake fraction in the size range of 1–3 μm. Findings also show that the assumption of a perfectly mixed environment could lead to an underestimation of the inhalation intake of particles by up to 3.2-fold. The results of this case study provide a basis for achieving more accurate personal inhalation exposure assessment and improved indoor air quality management.

Minimum Squared Error Method based on Modal Analysis for Acoustic Emission Source Location using Microfiber Coupler Sensor

This study focuses on an improved modal analysis method to the source location using a microfiber coupler sensor (MFCS). This method uses the Minimum Squared Error(MSE) to reduce the dependence of locating results on frequency selection and automate the locating process of contour threshold modal analysis. First, the secondary enclosed package method of MFCS was studied to ensure that the signal detected by this sensor meets the requirements of the contour threshold modal analysis and can extend the MFCS working life as well. The MSE method was then presented detailed. Second, the directionality experiment of the MFCS was performed to determine the best layout method for sensors in planar localization. The planar source localization method with both two sensors and three sensors are then introduced. Last, both linear and planar location experiments were performed on an aluminium thin plate. The results indicate that the sensor packaged by cuboid glass tube is more suitable for practical ultrasonic testing; the location process is automated by valid contour extraction and initial frequency selection, and the linear error using this method is effectively reduced as well; the best arrangement of the sensor is to place the sensor at the corner of the planar, and the angle between the sensor and the horizontal line is 45°; compared with two sensors, the planar localization with three sensors provides a more accurate and high repeatability results. This study proved that the combination of the MFCS and the MSE method is quite feasible and can be applied to the planar localization in plate-like structures effectively.

Inversion studies of CO2 emission sources in industrial parks

Industrial parks are a significant contributor to global CO2 emissions, making low-carbon development essential in mitigating climate change. In this study, we focus on the Hubei province industrial park as a case study and investigate the inversion of critical information on CO2 emission sources. Firstly, we establish a forward model of CO2 diffusion within the industrial park by employing atmospheric monitoring data and the AERMOD model system. Subsequently, we use a single back propagation (BP) neural network with the particle swarm optimization (PSO), whale optimization algorithm (WOA), and pelican optimization algorithm (POA) for inverse calculation of CO2 emission source locations and emission intensity. Our results show that the POA-BP inversion model has high inversion accuracy and stability, enabling us to locate the CO2 emission sources within the industrial park. This information can provide crucial decision support for the industrial park to tackle climate change and promote green transformation.

Method of Locating Loose Particles Inside Aerospace Equipment Based on Parameter-optimized XGBoost

The complex internal structure and heterogeneous composition material of aerospace equipment make it difficult to apply traditional acoustic emission source localization methods to the loose particle localization research. In previous studies, the authors transformed the loose particle localization problem into the multi-classification problem in machine learning, trained the loose particle localization model, but there were problems of low classification accuracy and low practicality. In this paper, the authors first introduced the ensemble learning idea into the loose particle detection field, designed a complete loose particle localization scheme, analyzed the above problems from multiple perspectives, and proposed new algorithms or strategies to enhance the superiority and practicability of the loose particle localization model. Specifically, in view of the low classification accuracy, the authors carried out the research on pulse preprocessing, feature engineering and model training from the four perspectives of signal, feature, data set and classifier, respectively. The zero-pulse-filling pulse matching algorithm and the channel-weighting-based feature selection method was newly proposed, Mel-Frequency Cepstral Coefficients features was newly extracted, feature optimization scheme was designed, and XGBoost ensemble classifier was trained. Thus, high-quality loose particle signals, high-quality localization data sets and high-performance loose particle localization models were obtained, respectively. Test results show that, the classification accuracy achieved by the new loose particle localization model was 96.80%, which was a significant improvement compared to the 83.53% achieved in previous studies. In view of the low practicality, the authors built the loose particle localization experimental system, gave the specific implementation steps of the loose particle localization method, as well as the general procedures for applying the loose particle localization model for physical testing. Meanwhile, taking into account the requirements of aerospace engineering applications, the authors added the majority voting strategy to convert classification results into localization results, thus newly proposed the definition of equipment-level loose particle localization accuracy. Several physical testing results show that, the loose particle localization model achieved a localization accuracy of 90.91%, which effectively verified the feasibility and stability of the proposed method. This study is an important supplement to the loose particle detection research, and of great significance in improving the reliability of aerospace systems.

Spatio-seasonal characterization and emissions estimation of ozone-depleting substances in the Pearl River Delta, China

Although chlorofluorocarbons (CFCs), CH3Br and chlorinated solvents such as CH3Cl3 and CCl4 have been entirely phased out in the Pearl River Delta (PRD) region, these compounds still exist chronically in atmosphere. In this study, ambient air samples were collected at twenty sites in the PRD region in winter (November to December 2019) and summer (August 2020) to characterize the spatio-seasonal variation of ozone-depleting substances (ODS), including CFC-11, CFC-12, CFC-113, CFC-114, CH3CCl3, CCl4, and CH3Br, and to estimate their emissions. Our observation showed that the average mixing ratios of the target ODS in the PRD region were 16.5%–92.5% lower compared with their reported levels in 2001 except for CFC-114, which basically remained at a constant level. Among the seven ODS, the average mixing ratio of CH3CCl3 declined most rapidly. The mixing ratios of the target ODS were higher in summer than in winter. Higher mixing ratios of the target ODS were observed in the two PRD urban background sites than the northern hemisphere background level, suggesting the existence of local emission sources. The spatial distribution indicated that the Foshan–Guangzhou–Dongguan–Shenzhen corridor area had the highest mixing ratios of the target ODS. Based on the CO-tracer ratio method, the estimated emissions of CFC-11, CFC-12, CFC-113, CFC-114, CH3Br, CH3CCl3 and CCl4 were 1.2, 0.8, 0.3, 0.2, 0.1, 0.1 and 0.4 kt/year, respectively. CFCs showed a very slow reduction in emission since 2008 in general, and the estimated emissions of CH3CCl3 and CCl4 gradually declined from the high value reported in 2004 while CH3Br kept the same level. The findings of this study can help to get a better understanding of the emissions status of ODS in the PRD region and provide valuable information for further reduction processes.