Urban Atmospheric Environment Research Articles

Metagenomic and Machine Learning Meta-Analyses Characterize Airborne Resistome Features and Their Hosts in China Megacities.

Urban ambient air contains a cocktail of antibiotic resistance genes (ARGs) emitted from various anthropogenic sites. However, what is largely unknown is whether the airborne ARGs exhibit site-specificity or their pathogenic hosts persistently exist in the air. Here, by retrieving 1.2 Tb metagenomic sequences (n = 136), we examined the airborne ARGs from hospitals, municipal wastewater treatment plants (WWTPs) and landfills, public transit centers, and urban sites located in seven of China's megacities. As validated by the multiple machine learning-based classification and optimization, ARGs' site-specificity was found to be the most apparent in hospital air, with featured resistances to clinical-used rifamycin and (glyco)peptides, whereas the more environmentally prevalent ARGs (e.g., resistance to sulfonamide and tetracycline) were identified being more specific to the nonclinical ambient air settings. Nearly all metagenome-assembled genomes (MAGs) that possessed the site-featured resistances were identified as pathogenic taxa, which occupied the upper-representative niches in all the neutrally distributed airborne microbial community (P < 0.01, m = 0.22-0.50, R2 = 0.41-0.86). These niche-favored putative resistant pathogens highlighted the enduring antibiotic resistance hazards in the studied urban air. These findings are critical, albeit the least appreciated until our study, to gauge the airborne dimension of resistomes' features and fates in urban atmospheric environments.

VOCs hyperspectral imaging: A new insight into evaluate emissions and the corresponding health risk from industries

The harm of VOCs emitted from industries to surrounding atmospheric environment and human health was well known and had received continuous attention. In order to improve the quality of urban atmospheric environment and the living environment of urban residents, a large number of original urban industries had been relocated to economically underdeveloped suburbs, which has significantly deteriorated the atmospheric environment in these areas and brought potential health risks to local vulnerable residents, which is actually an unfair manifestation under the background of economic development and ecological civilization construction. There were many residents near industrial parks, but there was a significant lack of VOCs monitoring equipment and data. At present, the time resolution of the most commonly used in situ method was seriously insufficient, and it was unable to quantify the diffusion/transport process of VOCs. It was urgent to have effective detection methods for industrial VOCs plume concentration and diffusion/transport process. In this study, we proposed a hyperspectral imaging technology, which can realize long-term continuous imaging monitoring on plume concentrations of formaldehyde (HCHO), glyoxal (CHOCHO) and benzaldehyde (C6H5CHO) and their corresponding diffusion processes. The deviation between the imaging and in situ sampling concentrations in the outlet was 4–19 %. The spatial resolution of this technique reached meter level, and the temporal resolution of one pixel was better than 20 s. In this study, we carried out hyperspectral imaging of aldehyde VOCs for a chemical facility, a petrochemical facility and an industrial park containing various types of enterprises in the Yangtze River Delta. The maximum observed concentration of HCHO was 120.44 ± 12.14 ug/m3 with the emission flux of 39.27 ± 3.97 g/h, which was emitted from a petrochemical facility in Shanghai. A diffusion/transport model was established, and we found that the spatial distribution of HCHO, CHOCHO and C6H5CHO for the chemical facility case in Shanghai were all mainly along the southeast-northwest direction during one year. The health risk assessment emphasized that residents within 10 km north of the outlet of the chemical facility in Shanghai should pay more attention to the health risks caused by industrial HCHO emissions. More systematically and comprehensively hyperspectral imaging of VOCs emissions for different types of enterprises and different processes were expected to performed to greatly promote the establishment of a dynamic emission inventory and an effective health risk evaluation system in the future.

Corrosion behaviors and mechanical properties of Q235 steel pipes collapsing due to corrosion after 25-year service in urban industrial atmospheric environment

Since the period required for testing the atmospheric corrosion of steel is long, most studies have focused on the corrosion behavior in less than three years, making exploring long-term atmospheric corrosion particularly valuable. This study investigates the corrosion behaviors, corrosion models, and mechanical properties of Q235 welded steel pipes collapsing due to corrosion after 25-year service in an urban industrial atmospheric environment using SEM, XRD, electrochemical methods, 3-D noncontact surface profilometry, and static tensile testing. The typical morphologies of α-FeOOH, γ-FeOOH, and Fe3O4 and the effect of the content of various corrosive materials on the corrosion rate of the steel pipes are identified and analyzed. Then, the influence of different corrosion degrees on the mechanical properties of the steel pipes is examined by considering the morphologies of the rust pits. The results show that the wetting–drying cycles affect the structures of the rust layers and their protective properties. The rust layers on the sheltered surface are thicker but less protective than those on the exposed surface. In the wet state, FeSO4 accelerates ion transfer, while Fe3O4 facilitates electron transfer. These processes collectively contribute to the corrosion of Q235 steel. Moreover, as the time-of-wetness of the rust layers on sheltered specimens increases, the corrosion rate of Q235 steel also accelerates. The corrosion model combines pitting and uniform corrosion and gradually changes from predominated by pitting to thickness as the corrosion degree increases. The corrosion morphology impacts the mechanical properties of the welded steel pipes, and the influences of the thickness loss and maximum rust pit depth on these properties are profound.

A deep spatio-temporal learning network for continuous citywide air quality forecast based on dense monitoring data

As urban air pollution becomes a severe environmental and societal issue globally, there is an increasing need on making air quality forecasts to prevent health and capital loss. Currently, most of the studies only provide air pollution forecasts on single or multiple sites, rather than citywide information. Due to complex urban structure and spatial transportation of air pollutants, the distribution of citywide air quality can be highly varying, which makes it extremely challenging to make accurate forecasts. Here, we address this challenge by exploring the potential of combining the dense air quality monitoring data with the deep learning technique. By using the fine-granular PM2.5 concentrations data collected from 417 micro monitoring stations in Lanzhou City, China, we proposed an end-to-end method to predict future citywide PM2.5 concentrations at 500 m × 500 m × 1 h resolution. This method consists of a spatio-temporal transformation component and a spatio-temporal prediction component, of which the former is responsible for converting PM2.5 concentration data into spatial-maps, while the spatio-temporal prediction network (i.e., Air-PredNet in our study) based on ConvLSTM in the latter is responsible for capturing the spatio-temporal characterizations in the spatial-map sequences and finally generating citywide air quality forecasts. The results show that our method can accurately predict the spatial distribution and variations of PM2.5 concentration with an RMSE of 2.12 μg/m3 for 1-h prediction and an RMSE of 4.09 μg/m3 for 6-h prediction. In addition, we also proved the robustness of Air-PredNet based on the prediction performance for O3 and NO2. Our results demonstrate the potential and feasibility of dense monitoring data combined with advanced data science methods for citywide urban air quality forecast, which supports the urban atmospheric environment fine management and decision-making.

Application of a Deep Learning Fusion Model in Fine Particulate Matter Concentration Prediction

With the rapid development of urbanization, ambient air pollution is becoming increasingly serious. Out of many pollutants, fine particulate matter (PM2.5) is the pollutant that affects the urban atmospheric environment to the greatest extent. Fine particulate matter (PM2.5) concentration prediction is of great significance to human health and environmental protection. This paper proposes a CNN-SSA-DBiLSTM-attention deep learning fusion model. This paper took the meteorological observation data and pollutant data from eight stations in Bijie from 1 January 2015 to 31 December 2022 as the sample data for training and testing. For the obtained data, the missing values and the data obtained from the correlation analysis performed were first processed. Secondly, a convolutional neural network (CNN) was used for the feature selection. DBILSTM was then used to establish a network model for the relationship between the input and actual output sequences, and an attention mechanism was added to enhance the impact of the relevant information. The number of units in the DBILSTM and the epoch of the whole network were optimized using the sparrow search algorithm (SSA), and the predicted value was the output after optimization. This paper predicts the concentration of PM2.5 in different time spans and seasons, and makes a comparison with the CNN-DBILSTM, BILSTM, and LSTM models. The results show that the CNN-SSA-DBiLSTM-attention model had the best prediction effect, and its accuracy improved with the increasing prediction time span. The coefficient of determination (R2) is stable at about 0.95. The results revealed that the proposed CNN-SSA-DBiLSTM-attention ensemble framework is a reliable and accurate method, and verifies the research results of this paper in regard to the prediction of PM2.5 concentration. This research has important implications for human health and environmental protection. The proposed method could inspire researchers to develop even more effective methods for atmospheric environment pollution modeling.

Vehicle Pollutant Dispersion in the Urban Atmospheric Environment: A Review of Mechanism, Modeling, and Application

In recent years, vehicle emissions have become one of the important pollutant sources of the urban atmosphere. Scholars and decision-makers are constantly expected to accurately grasp the dispersion of vehicle pollutants to formulate a series of policies and strategies which can facilitate a friendly and sustainable urban environment, such as controlling the total number of vehicles, requiring higher emission standards, promoting new energy vehicles, improving public transit service, and optimizing non-motorized transportation systems. This paper provides a review of the mechanism research methods and mathematical modeling approaches for urban vehicle pollutant dispersion. The mechanism research methods reviewed include field measurements, wind tunnel experiments, and numerical simulations. The modeling approaches involve two kinds of popular models: Box models (STREET, CPBM, AURORA, PBM) and Gaussian models (CALINE, HIWAY, OSPM, CALPUFF, R-LINE, ADMS series, EPISODE, CityChem, SIRANE, MUNICH). Moreover, this paper clarifies the basic assumption, fundamental principle, related research, applicable conditions, and limitations of these mechanism research methods and modeling approaches.

Fine particulate pollution driven by nitrate in the moisture urban atmospheric environment in the Pearl River Delta region of south China

To identify potential sources of fine particles (PM2.5, with aerodynamic diameter (Da) ≤ 2.5 μm) in urban Dongguan of south China, a comprehensive campaign was carried out in the whole 2019. Hourly PM2.5 and its dominant chemical components including organic carbon (OC), elemental carbon (EC), water-soluble inorganic ions (WSIIs) and thirteen elements were measured using online instruments. Gaseous pollutants including NH3, HNO3, NO2, NO and O3 and meteorological parameters were also synchronously measured. PM2.5 was dominated by carbonaceous aerosols in summer and by WSIIs in the other seasons. PM2.5 and its dominant chemical components mostly peaked around noon (10:00–14:00 LST). Furthermore, high PM2.5 levels during the daytime were closely related with the increased NO3− levels. The high mass concentrations of NO3− in urban Dongguan during the daytime were likely related with regional transport of NO3− from suburban Dongguan, which was originated from the reaction between NO2 and O3 under the moisture condition during the nighttime. Seven major source factors for PM2.5 including secondary sulfate, ship emission, traffic emission, secondary nitrate, industrial processes, soil dust and coal combustion were identified by positive matrix factorization (PMF) analysis, which contributed 26 ± 14%, 16 ± 16%, 16 ± 10%, 14 ± 11%, 12 ± 11%, 8 ± 6% and 8 ± 6%, respectively, to annual PM2.5 mass concentration. Although secondary sulfate contributed much more than secondary nitrate to PM2.5 on annual basis, the latter exceeded the former source factor when daily PM2.5 mass concentration was higher than 60 μg m−3, indicating the critical role nitrate played in PM2.5 episode events.

Dust amount reaching typical cities from dust emissions due to soil wind erosion in Beijing-Tianjin sandstorm source regions and its source analysis

<p indent="0mm">Dust emissions caused by soil wind erosion are a significant and concerning source of respirable particles in the atmospheric environment. Accurately estimating the contribution of dust emissions resulting from soil wind erosion to inhalable particulate matter in the urban atmospheric environment is required for the central government to formulate atmospheric environmental governance plans and decisions. By combining ground investigation, field observation, remote sensing image interpretation and other strategies, this paper first estimated the dust amount of four different particle sizes of 0–2.5, 2.5–5, 5–10, and <sc>10–20 μm</sc> arriving in Beijing, Zhangjiakou, Chifeng, Datong, Huhehaote, Xilinhaote, Baotou and Yulin, which derived from dust emissions caused by soil wind erosion due to wind force in Beijing-Tianjin sandstorm source regions. The total dust amounts reaching above cities were 274.49×10⁴, 146.96×10⁴, 151.83×10⁴, 91.03×10⁴, 68.61×10⁴, 65.42×10⁴, 41.46×10⁴, and <sc>26.21×10⁴ t,</sc> respectively. Among these dust amounts, PM_2.5 and PM₁₀ accounted for 35.40% and 84.16%, respectively. To improve the living environment, the Beijing-Tianjin sandstorm source control project targets both PM₁₀ and PM_2.5, with an emphasis on PM_2.5. Second, the contributions of subregions in the Beijing-Tianjin sandstorm source to dust amounts of different particle sizes for each city were distinctly different. For the contributions of each subregion to the total dust amount for above eight cities, the maximum came from the subregion of interlaced belt of agriculture and animal husbandry, followed by the subregions of desert steppe, the Yanshan Hills water source reserve, the mountainous and hilly areas in northern Shanxi, Hunshandake sandy land, the Greater Khingan Mountains, Kubuqi desert, Horqin sandy land, the typical steppe and Mu Us Sandy Land. The cumulative contributions for the first three subregions and four deserts (of sandy land) were 80.52% and 9.84%, respectively. The former’s cumulative contributions to the dust amounts of four particle sizes ranged from 73.06% to 83.40%, respectively, while the latter’s cumulative contributions were only between 5.56% and 12.42%. These indicated that the management of the Beijing-Tianjin sandstorm sources should focus on the subregions of the interlaced belt agriculture and animal husbandry, desert steppe and the Yanshan Hills water source reserve rather than desert (of sandy land) subregions. Third, the contributions of the cultivated land, desert (or sandy land) and grass and shrub land to the total dust amounts for above eight cities were 576.27×10⁴, 186.30×10⁴, and <sc>103.44×10⁴ t,</sc> respectively. At the same time, their contributions to the dust amounts of 0–2.5, 2.5–5, 5–10, and <sc>10–20 μm</sc> were 138.18×10⁴, 94.42×10⁴, 182.54×10⁴ and <sc>161.14×10⁴ t;</sc> 23.16×10⁴, 12.44×10⁴, 41.89×10⁴ and <sc>8.81×10⁴ t;</sc> and 28.94×10⁴, 20.79×10⁴, 42.65×10⁴, <sc>11.06×10⁴ t,</sc> respectively. Hence, the management of the Beijing-Tianjin sandstorm sources should focus on the management of cultivated land, followed by desert (or sandy land) and finally grass and shrub land. In addition, the dust amounts reaching above eight cities mainly derived from the westward wind direction, such as SW, WSW, W, WNW, NW, and NNW, although a few came from N and NNE directions. 85.15%–93.28% of dust in Baotou, Beijing, Datong, Huhehaote and Zhangjiakou originated from W, WNW, and NW; 96.16% of dust in Chifeng originated from W, WNW, and WSW; 93.68% of dust in Xilinhaote originated from WSW, W, SW, and WNW; and 81.16% of dust in Chifeng originated from NW, NNW, and NNE. There were obvious differences in the contribution of wind direction to the dust amount in different particle size groups in each city. These results provide a basis for the optimization and adjustment of the Beijing-Tianjin sandstorm source control project.

Influence of Urban Atmospheric Ecological Environment on the Development of Outdoor Sports

In recent years, the process of urbanization in China has developed rapidly. However, many problems have also appeared, such as a rapid population increase, resource shortage, and deterioration of the ecological environment. The deterioration of the ecological environment has led to increased air pollution and noise pollution, threatening human health, and hindering outdoor sports. This paper takes a certain city outdoor sports venue as the research object and studies the influence of the urban atmospheric ecological environment on the development of outdoor sports through the method of questionnaire survey and mathematical statistics. The results show that the city’s air quality index in 2019 was “excellent” and “good” for a total of 240 days, the greening rate reached 35%, the noise decibels were between 38 dB and 67 dB, and the ecological environment was good. According to statistics, 5.8% of students, 24.23% of middle-aged people, and 37.24% of elderly people in the city who engaged in outdoor sports often experience physical discomfort. After launching outdoor sports in this atmospheric ecological environment, 49.84% of students, 44.59% of office workers, and 39.65% of elderly people feel that their body and mind have improved. A good urban ecological environment can improve the physical and mental health of outdoor sports personnel. Through outdoor sports, the physical and mental health of all sports groups in the ecological environment has been improved. According to the location of this study, the subjects generally said that in the good atmospheric ecological environment of the city, outdoor sports improved physical and mental health.

High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine Learning.

Spatially explicit urban air quality information is important for urban fine-management and public life. However, existing air quality measurement methods still have some limitations on spatial coverage and system stability. A micro station is an emerging monitoring system with multiple sensors, which can be deployed to provide dense air quality monitoring data. Here, we proposed a method for urban air quality mapping at high-resolution for multiple pollutants. By using the dense air quality monitoring data from 448 micro stations in Lanzhou city, we developed a decision tree model to infer the distribution of citywide air quality at a 500 m × 500 m × 1 h resolution, with a coefficient of determination (R2) value of 0.740 for PM2.5, 0.754 for CO and 0.716 for SO2. Meanwhile, we also show that the deployment density of the monitoring stations can have a significant impact on the air quality inference results. Our method is able to show both short-term and long-term distribution of multiple important pollutants in the city, which demonstrates the potential and feasibility of dense monitoring data combined with advanced data science methods to support urban atmospheric environment fine-management, policy making, and public health studies.

Can the Relationship between Atmospheric Environmental Quality and Urban Industrial Structure Adjustment Achieve Green and Sustainable Development in China? A Case of Taiyuan City

Atmospheric environmental quality affects the high quality and sustainable development of the economy. The optimisation and upgrading of the industrial system are important to improve the operation efficiency of the economy and society. Firstly, this paper constructs the theoretical analysis framework of coupling and coordination between the atmospheric environment system and the industrial system and analyses the internal mechanism of the interaction and coordinated development of the two systems. Then, it puts forward the combination of the coupling coordination model and the VAR model (Vector autoregressive model) and presents the analysis and evaluation method of the relationship between them from the two perspectives of “static” and “dynamic”. Finally, the empirical study is conducted in Taiyuan, a resource-based city in China. The results show that: (1) The two systems in Taiyuan have an obvious interaction and develop in the direction of benign coupling. (2) The impact of the two systems on each other is mainly in the medium and long term and dominated by the role of the atmospheric environment system on the industrial system. This study provides a theoretical framework and evaluation methods for evaluating and analysing the relationship between the urban atmospheric environment system and the industrial system in China, and then provides suggestions for policymaking.

Multiple pathways and mediation effects of built environment on kidney disease rate via mitigation of atmospheric threats

Air pollution and high temperatures can increase kidney disease rate, especially under climate change. A well-designed urban environment has mediating effects on atmospheric environmental threats and promoting human health, but previous studies have overlooked these effects. This study used partial least squares modeling and urban-scale data from Taiwan to identify the crucial effects (i.e., direct, indirect, and total effects) and pathways of urban form (i.e., urban development intensity, land-use mix, and urban sprawl), urban greening (i.e., green coverage), urban industrial status (e.g., industrial level), atmospheric environment (i.e., high temperature and air pollution), and socioeconomic status (i.e., elderly ratio, medical resources, and economic status) on kidney disease rate. Maximizing land-use mix and green coverage and minimizing urban development intensity, urban sprawl, and industrial levels could help reduce kidney disease rate. Air pollution and high temperature had a mediation effect of built environment on kidney disease rate; with the mediation effect of air pollution was greater than that of high temperature. Furthermore, air pollution, high temperature, and elderly ratio increased kidney disease rate, whereas medical resources decreased kidney disease rate. This study is the first to consider the impact (i.e., direct, indirect, and total effects) and pathways of built environment characteristics on kidney disease rate. The findings revealed that an appropriate urban policy might be a practical strategy and lower kidney disease rate for a healthy city development. Moreover, this study provides a new approach for clarifying complex relationships and identifying crucial factors.

Exploring the Spatiotemporal Evolution and Socioeconomic Determinants of PM2.5 Distribution and Its Hierarchical Management Policies in 366 Chinese Cities.

From 2013 to 2017, progress has been made by implementing the Air Pollution Prevention and Control Action Plan. Under the background of the 3 Year Action Plan to Fight Air Pollution (2018–2020), the pollution status of PM2.5, a typical air pollutant, has been the focus of continuous attention. The spatiotemporal specificity of PM2.5 pollution in the Chinese urban atmospheric environment from 2018 to 2020 can be summarized to help conclude and evaluate the phased results of the battle against air pollution, and further, contemplate the governance measures during the period of the 14th Five-Year Plan (2021–2025). Based on PM2.5 data from 2018 to 2020 and taking 366 cities across China as research objects, this study found that PM2.5 pollution has improved year by year from 2018 to 2020, and that the heavily polluted areas were southwest Xinjiang and North China. The number of cities with a PM2.5 concentration in the range of 25–35 μg/m3 increased from 34 in 2018 to 86 in 2019 and 99 in 2020. Moreover, the spatial variation of the PM2.5 gravity center was not significant. Concretely, PM2.5 pollution in 2018 was more serious in the first and fourth quarters, and the shift of the pollution's gravity center from the first quarter to the fourth quarter was small. Global autocorrelation indicated that the space was positively correlated and had strong spatial aggregation. Local Moran's I and Local Geti's G were applied to identify hotspots with a high degree of aggregation. Integrating national population density, hotspots were classified into four areas: the Beijing–Tianjin–Hebei region, the Fenwei Plain, the Yangtze River Delta, and the surrounding areas were selected as the key hotspots for further geographic weighted regression analysis in 2018. The influence degree of each factor on the average annual PM2.5 concentration declined in the following order: (1) the proportion of secondary industry in the GDP, (2) the ownership of civilian vehicles, (3) the annual grain planting area, (4) the annual average population, (5) the urban construction land area, (6) the green space area, and (7) the per capita GDP. Finally, combined with the spatiotemporal distribution of PM2.5, specific suggestions were provided for the classified key hotspots (Areas A, B, and C), to provide preliminary ideas and countermeasures for PM2.5 control in deep-water areas in the 14th Five-Year Plan.

Energy Consumption Analysis Model of Typical Cogeneration Systems

At present, China’s urban heating system consumes a lot of energy and is seriously polluted. Our government is working hard to develop urban natural gas regional heating systems to replace traditional coal-fired heating to reduce the serious impact of coal combustion emissions on the urban atmospheric environment during the heating season. On this basis, the characteristics of traditional energy efficiency comparison methods and the problems encountered by these traditional methods in the energy efficiency analysis and application of distributed energy cold, hot and power multigeneration systems in China are analyzed, and the comparable performance efficiency analysis methods suitable for the application of cold, hot and hot power multiple production applications of distributed energy are studied.

Fine Particulate Pollution Driven by Nitrogen Dioxide and Ozone in the Moisture Urban Atmospheric Environment in Pearl River Delta Region of South China

Fine Particulate Pollution Driven by Nitrogen Dioxide and Ozone in the Moisture Urban Atmospheric Environment in Pearl River Delta Region of South China

Fine Particulate Pollution Driven by Nitrate in the Moisture Urban Atmospheric Environment in Pearl River Delta Region of South China

Fine Particulate Pollution Driven by Nitrate in the Moisture Urban Atmospheric Environment in Pearl River Delta Region of South China

A one-year study on black carbon in urban Beijing: Concentrations, sources and implications on visibility

A one-year field measurement was conducted at an urban atmospheric environment monitoring station in Beijing. The concentrations, sources and implications on visibility of black carbon (BC) during different periods were investigated. The average BC concentration was 2.8 ± 2.5, 3.2 ± 1.9, 4.4 ± 3.6 and 5.3 ± 4.5 μg m−3 in spring, summer, autumn and winter, respectively. The source apportionment presented liquid fuel (traffic) was the main source of BC in spring (77.4%), summer (86.8%) and autumn (70.8%), while solid fuel (coal and biomass burning) dominated in winter (64.3%), implying regional air transport occurred because of the limited combustion activities in urban Beijing. Notably, the contribution of solid fuel decreased from Ⅲ (4 ≤ Vis <6 km) to Ⅴ (0 < Vis <2 km), indicating the effect of regional air transport weakened during the wintertime severe haze pollution. Combining the results of PSCF, BC during the winter haze period was not only influenced by the air masses from southern regions but also western areas, e.g., Datong city, where coal is still widely used. The ratio of σabs/Bext (light absorption coefficient/atmospheric extinction coefficient) during the haze periods was higher than that of non-haze periods in spring, autumn and winter, proving higher BC concentration favored the decline in visibility. However, in all seasons, the decreasing ratio of σabs/Bext with the haze pollution getting worse, indicated that BC contributed less and other components (e.g., SO42−, NO3− and organics) contributed more to the visibility reduction when severe haze event occurred.

Leaf reflectance and functional traits as environmental indicators of urban dust deposition

BackgroundHow to quickly predict and evaluate urban dust deposition is the key to the control of urban atmospheric environment. Here, we focus on changes of plant reflectance and plant functional traits due to dust deposition, and develop a prediction model of dust deposition based on these traits.ResultsThe results showed that (1) The average dust deposition per unit area of Ligustrum quihoui leaves was significantly different among urban environments (street (18.1001 g/m2), community (14.5597 g/m2) and park (9.7661 g/m2)). Among different urban environments, leaf reflectance curves tends to be consistent, but there were significant differences in leaf reflectance values (park (0.052–0.585) > community (0.028–0.477) > street (0.025–0.203)). (2) There were five major reflection peaks and five major absorption valleys. (3) The spectral reflectances before and after dust removal were significantly different (clean leaves > dust-stagnant leaves). 695 ~ 1400 nm was the sensitive range of spectral response. (4) Dust deposition has significant influence on slope and position of red edge. Red edge slope was park > community > street. After dust deposition, the red edge position has obviously “blue shift”. The moving distance of the red edge position increases with the increase of dust deposition. The forecast model of dust deposition amount established by simple ratio index (y = 2.517x + 0.381, R2 = 0.787, RMSE (root-mean-square error) = 0.187. In the model, y refers to dust retention, x refers to simple ratio index.) has an average accuracy of 99.98%. (5) With the increase of dust deposition, the specific leaf area and chlorophyll content index decreased gradually. The leaf dry matter content, leaf tissue density and leaf thickness increased gradually.ConclusionIn the dust-polluted environment, L. quihoui generally presents a combination of characters with lower specific leaf area, chlorophyll content index, and higher leaf dry matter content, leaf tissue density and leaf thickness. Leaf reflectance spectroscopy and functional traits have been proved to be effective in evaluating the changes of urban dust deposition.

A health risk-based threshold method to evaluate Urban Atmospheric Environment Carrying Capacity in Beijing-Tianjin-Hebei (BTH) Region

Urban atmospheric environment protection is an important strategy for sustainable urban development. Nevertheless, serious atmospheric pollution problems caused by socioeconomic activities have become a global issue, which brings enormous pressure on human health. However, as the core of urban sustainability, the human health risk is not adopted as an element to evaluate the Urban Atmospheric Environment Carrying Capacity (UAECC) in previous studies. Therefore, this study develops an improved threshold method called the health risk-based threshold method to evaluate UAECC, by adding the principle of the Air Quality Health Index (AQHI) into the traditional threshold method. The effectiveness of the novel UAECC method is demonstrated by using the data collected from a sample of 13 cities in the Beijing-Tianjin-Hebei (BTH) region. Moreover, the case demonstration considers both the microscopic and macroscopic UAECC. The demonstration results suggest that (1) poor performance of UAECC happens in urban built-up areas; (2) the UAECC performance of cities located in the north part of BTH region behaves better than that in the south part; (3) PM2.5, PM10, and NO2 are three primary contributors to the UAECC performance in the BTH region; (4) the UAECC performance in the BTH region has been improved, while the single-factor of O3 deteriorated from 2015 to 2020. Furthermore, the evaluation results by using the improved method can provide valuable references for formulating tail-made policies towards different cities with different UAECC conditions.

GFIT3: a full physics retrieval algorithm for remote sensing of greenhouse gases in the presence of aerosols

Abstract. Remote sensing of greenhouse gases (GHGs) in cities, where high GHG emissions are typically associated with heavy aerosol loading, is challenging due to retrieval uncertainties caused by the imperfect characterization of scattering by aerosols. We investigate this problem by developing GFIT3, a full physics algorithm to retrieve GHGs (CO2 and CH4) by accounting for aerosol scattering effects in polluted urban atmospheres. In particular, the algorithm includes coarse- (including sea salt and dust) and fine- (including organic carbon, black carbon, and sulfate) mode aerosols in the radiative transfer model. The performance of GFIT3 is assessed using high-spectral-resolution observations over the Los Angeles (LA) megacity made by the California Laboratory for Atmospheric Remote Sensing Fourier transform spectrometer (CLARS-FTS). CLARS-FTS is located on Mt. Wilson, California, at 1.67 km a.s.l. overlooking the LA Basin, and it makes observations of reflected sunlight in the near-infrared spectral range. The first set of evaluations are performed by conducting retrieval experiments using synthetic spectra. We find that errors in the retrievals of column-averaged dry air mole fractions of CO2 (XCO2) and CH4 (XCH4) due to uncertainties in the aerosol optical properties and atmospheric a priori profiles are less than 1 % on average. This indicates that atmospheric scattering does not induce a large bias in the retrievals when the aerosols are properly characterized. The methodology is then further evaluated by comparing GHG retrievals using GFIT3 with those obtained from the CLARS-GFIT algorithm (used for currently operational CLARS retrievals) that does not account for aerosol scattering. We find a significant correlation between retrieval bias and aerosol optical depth (AOD). A comparison of GFIT3 AOD retrievals with collocated ground-based observations from AErosol RObotic NETwork (AERONET) shows that the developed algorithm produces very accurate results, with biases in AOD estimates of about 0.02. Finally, we assess the uncertainty in the widely used tracer–tracer ratio method to obtain CH4 emissions based on CO2 emissions and find that using the CH4/CO2 ratio effectively cancels out biases due to aerosol scattering. Overall, this study of applying GFIT3 to CLARS-FTS observations improves our understanding of the impact of aerosol scattering on the remote sensing of GHGs in polluted urban atmospheric environments. GHG retrievals from CLARS-FTS are potentially complementary to existing ground-based and spaceborne observations to monitor anthropogenic GHG fluxes in megacities.