A review of the technology and applications of methods for evaluating the transport of air pollutants

A variety of methods based on air quality models, including tracer methods, the brute-force method (BFM), decoupled direct method (DDM), high-order decoupled direct method (HDDM), response surface models (RSMs) and so on forth, have been widely used to study the transport of air pollutants. These methods have good applicability for the transport of air pollutants with simple formation mechanisms. However, differences in research conclusions on secondary pollutants with obvious nonlinear characteristics have been reported. For example, the tracer method is suitable for the study of simplified scenarios, while HDDM and RSMs are more suitable for the study for nonlinear pollutants. Multiple observation techniques, including conventional air pollutant observation, lidar observation, air sounding balloons, vehicle-mounted and ship-borne technology, aerial surveys, and remote sensing observations, have been utilized to investigate air pollutant transport characteristics with time resolution as high as 1 sec. In addition, based on a multi-regional input-output model combined with emission inventories, the transfer of air pollutant emissions can be evaluated and applied to study the air pollutant transport characteristics. Observational technologies have advantages in temporal resolution and accuracy, while modeling technologies are more flexible in spatial resolution and research plan setting. In order to accurately quantify the transport characteristics of pollutants, it is necessary to develop a research method for interactive verification of observation and simulation. Quantitative evaluation of the transport of air pollutants from different angles can provide a scientific basis for regional joint prevention and control.

Estimation of high spatial resolution ground-level ozone concentrations based on Landsat 8 TIR bands with deep forest model

In recent years, China has been facing severe ozone (O₃) pollution, which poses a remarkable threat to human health. Most estimation methods only provide ozone products at a relatively coarse resolution, such as 5 km, but high-resolution ozone data are essential for ozone pollution prevention and control. To this end, we proposed a new framework for estimating ozone concentrations at 300 m resolution in China based on Landsat 8 infrared (IR) bands and meteorological data using a deep forest (DF) model. DF combines the excellent performance of tree integration with the expressive power of hierarchical distributed representations of neural networks. The accuracy and mapping results of DF are considerably better than some widely used machine learning methods (generalized regression neural regression network and random forest). The sample-based cross-validation (CV), station-based CV, time-based CV, and extrapolation validation show that the estimations of DF are in high agreement with the station observations with determination coefficient values of 0.938, 0.926, 0.687, and 0.660, respectively. The proposed method was used to analyze the spatial and temporal ozone variations at fine scales in three typical Chinese cities (Beijing, Wuhan and Guangzhou), where the mean ozone concentrations during the polluted season are consistent with the land use and urban heat island distribution. The rationality of ozone estimates was verified, and the advantages of high-resolution mapping was demonstrated by comparing the monitoring data from municipal controlling stations in Beijing, 10 km ozone products, and satellite images. Our product can represent spatial details and locate local pollution sources, such as temples. The proposed method has important implications for the fine-scale monitoring of ozone pollution.

Identification of NO2 and SO2 Pollution Hotspots and Sources in Jiangsu Province of China

Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are important atmospheric trace gases for determining air quality, human health, climate change, and ecological conditions both regionally and globally. In this study, the Ozone Monitoring Instrument (OMI), total column nitrogen dioxide (NO2), and sulfur dioxide (SO2) were used from 2005 to 2020 to identify pollution hotspots and potential source areas responsible for air pollution in Jiangsu Province. The study investigated the spatiotemporal distribution and variability of NO2 and SO2, the SO2/NO2 ratio, and their trends, and potential source contribution function (PSCF) analysis was performed to identify potential source areas. The spatial distributions showed higher values (>0.60 DU) of annual mean NO2 and SO2 for most cities of Jiangsu Province except for Yancheng City (<0.50 DU). The seasonal analyses showed the highest NO2 and SO2 in winter, followed by spring, autumn, and summer. Coal-fire-based room heating and stable meteorological conditions during the cold season may cause higher NO2 and SO2 in winter. Notably, the occurrence frequency of NO2 and SO2 of >1.2 was highest in winter, which varied between 9.14~32.46% for NO2 and 7.84~21.67% for SO2, indicating a high level of pollution across Jiangsu Province. The high SO2/NO2 ratio (>0.60) indicated that industry is the dominant source, with significant annual and seasonal variations. Trends in NO2 and SO2 were calculated for 2005–2020, 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan (FYP)), 2011–2015 (during the 12th FYP), and 2013–2017 (the Action Plan of Air Pollution Prevention and Control (APPC-AC)). Annually, decreasing trends in NO2 were more prominent during the 12th FYP period (2011–2015: −0.024~−0.052 DU/year) than in the APPC-AC period (2013–2017: −0.007~−0.043 DU/year) and 2005–2020 (−0.002 to −0.012 DU/year). However, no prevention and control policies for NO2 were included during the 11th FYP period (2006–2010), resulting in an increasing trend in NO2 (0.015 to 0.031) observed throughout the study area. Furthermore, the implementation of China’s strict air pollution control policies caused a larger decrease in SO2 (per year) during the 12th FYP period (−0.002~−0.075 DU/year) than in the 11th FYP period (−0.014~−0.071 DU/year), the APPC-AC period (−0.007~−0.043 DU/year), and 2005–2020 (−0.015~−0.032 DU/year). PSCF analysis indicated that the air quality of Jiangsu Province is mainly influenced by local pollution sources.

Study on Comprehensive Assessment of Environmental Impact of Air Pollution

Pollutants discharged from irrational energy consumption pose a serious threat to urban ecological security. The Western Taiwan Straits Economic Zone is an important part of China’s coastal economy. With the rapid development of the economy in this area, the atmospheric environmental pollution problem, caused by energy consumption, has become increasingly serious. Therefore, the study of the environmental impact assessment of air pollution in the Western Taiwan Straits Economic Zone has reference value to prevent ecological risks. This paper constructed a regional-scale environmental impact assessment model that includes pollution sources, pollution stress, and evaluation results, and evaluated the environmental impact of SO2, NO2, CO, PM10, and PM2.5 from three perspectives: regional integration, different energy consumption sectors, and different cities. The results showed that the regional environmental impact level of the research area was high, and the main pollutants transformed from SO2 to NO2, PM10, and PM2.5 from 2008 to 2016. According to the results of different sectors, the transportation sector contributes the most to NO2 and remains unchanged, and the industrial sector contributes the most to SO2, PM10, and PM2.5. Combined with the research results of different cities, cities concentrated in the coastal areas contribute more pollution than other cities do.

Chemical composition, source, and process of urban aerosols during winter haze formation in Northeast China

The characteristics of aerosol particles have been poorly evaluated even though haze episodes frequently occur in winter in Northeast China. OC/EC analysis, ion chromatography, and transmission electron microscopy (TEM) were used to investigate the organic carbon (OC) and elemental carbon (EC), and soluble ions in PM_2.5 and the mixing state of individual particles during a severe wintertime haze episode in Northeast China. The organic matter (OM), NH₄⁺, SO₄^2-, and NO₃^- concentrations in PM_2.5 were 89.5 μg/m³, 24.2 μg/m³, 28.1 μg/m³, and 32.8 μg/m³ on the haze days, respectively. TEM observations further showed that over 80% of the haze particles contained primary organic aerosols (POAs). Based on a comparison of the data obtained during the haze formation, we generate the following synthetic model of the process: (1) Stable synoptic meteorological conditions drove the haze formation. (2) The early stage of haze formation (light or moderate haze) was mainly caused by the enrichment of POAs from coal burning for household heating and cooking. (3) High levels of secondary organic aerosols (SOAs), sulfates, and nitrates formation via heterogeneous reactions together with POAs accumulation promoted to the evolution from light or moderate to severe haze. Compared to the severe haze episodes over the North China Plain, the PM_2.5 in Northeast China analyzed in the present study contained similar sulfate, higher SOA, and lower nitrate contents. Our results suggest that most of the POAs and secondary particles were likely related to emissions from coal-burning residential stoves in rural outskirts and small boilers in urban areas. The inefficient burning of coal for household heating and cooking should be monitored during wintertime in Northeast China.

SO2 Emissions in China - Their Network and Hierarchical Structures

SO₂ emissions lead to various harmful effects on environment and human health. The SO₂ emission in China has significant contribution to the global SO₂ emission, so it is necessary to employ various methods to study SO₂ emissions in China with great details in order to lay the foundation for policymaking to improve environmental conditions in China. Network analysis is used to analyze the SO₂ emissions from power generation, industrial, residential and transportation sectors in China for 2008 and 2010, which are recently available from 1744 ground surface monitoring stations. The results show that the SO₂ emissions from power generation sector were highly individualized as small-sized clusters, the SO₂ emissions from industrial sector underwent an integration process with a large cluster contained 1674 places covering all industrial areas in China, the SO₂ emissions from residential sector was not impacted by time, and the SO₂ emissions from transportation sector underwent significant integration. Hierarchical structure is obtained by further combining SO₂ emissions from all four sectors and is potentially useful to find out similar patterns of SO₂ emissions, which can provide information on understanding the mechanisms of SO₂ pollution and on designing different environmental measure to combat SO₂ emissions.

Case study of the Asian dust and pollutant event in spring 2006: source, transport, and contribution to Taiwan

Surface measurements and a regional dust model were used to analyze the source, transport, and contribution of a dust event transporting with aerosol pollutant over Taiwan from 16 to 19 March, 2006. During the event, the hourly aerosol concentrations reached close to 400 μg m(-3) in northern Taiwan, and approximately 300 μg m(-3) in other areas of the island. Trajectory and regional dust models show that the dust event originated in eastern Mongolia and northern China, and the dust layer can descend from 2 to 3 km in the source area to below 1.5 km over Taiwan. On the other hand, model results show that pollution was transported near the surface from coastal China to Taiwan. During this dust event, polluted aerosol was first observed over northern Taiwan right after a frontal passage, and the concentration was strongly enhanced following the passage of the light rainfall 12h later. The descent of dusty air from the free troposphere lagged the arrival of polluted air by 7h, and was partially mixed with polluted aerosol when the transport decelerated over Taiwan. During the event, dust particles accounted for up to 60% of observed particulate matter less than 10 μm (PM10) over Taiwan, but decreased to less than 35% for particulate matter less than 2.5 μm (PM2.5) over most areas of the island. On the other hand, the long-range transport of non-dust aerosols, mainly anthropogenic pollutants, accounted for close to 30% of observed PM10 concentration in northern and western Taiwan prior to dust arrival, and the contribution of PM2.5 increased to close to 40% over the same areas. Local emission of aerosols accounted for less than 25% of PM10 concentrations in northern Taiwan, but was about 60% for PM2.5 in central and southern Taiwan because these areas are less influenced by long-range transport.

Vertical transport of ozone and CO during super cyclones in the Bay of Bengal as detected by Tropospheric Emission Spectrometer

Vertical profiles of carbon monoxide (CO) and ozone retrieved from Tropospheric Emission Spectrometer have been analyzed during two super cyclone systems Mala and Sidr. Super cyclones Mala and Sidr traversed the Bay of Bengal (BOB) region on April 24-29, 2006 and November 12-16, 2007 respectively. The CO and ozone plume is observed as a strong enhancement of these pollutants in the upper troposphere over the BOB, indicating deep convective transport. Longitude-height cross-section of these pollutants shows vertical transport to the upper troposphere. CO mixing ratio ~90 ppb is observed near the 146-mb level during the cyclone Mala and near 316 mb during the cyclone Sidr. Ozone mixing ratio ~60-100 ppb is observed near the 316-mb level during both the cyclones. Analysis of National Centers for Environmental Prediction (NCEP) reanalysis vertical winds (omega) confirms vertical transport in the BOB.