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The single-beam bathymetric light detection and ranging (LiDAR) system 1 (SBLS-1), which is equipped with a 532-nm-band laser projector and two concentric-circle receivers for shallow- and deep-water echo signals, is a lightweight and convenient prototype instrument with low energy consumption. In this study, a novel LiDAR bathymetric method is utilized to achieve single-beam and dual-channel bathymetric characteristics, and an adaptive extraction method is proposed based on the cumulative standard deviation of the peak and trough, which is mainly used to extract the signal segment and eliminate system and random noise. To adapt the dual-channel bathymetric mechanism, an automatic channel-selection method was used at various water depths. A minimum half-wavelength Gaussian iterative decomposition is proposed to improve the detection accuracy of the surface- and bottom-water waveform components and ensure bathymetric accuracy and reliability. Based on a comparison between the experimental results and in situ data, it was found that the SBLS-1 obtained a bathymetric accuracy and RMSE of 0.27 m and 0.23 m at the Weifang and Qingdao test fields. This indicates that the SBLS-1 was bathymetrically capable of acquiring a reliable, high-efficiency waveform dataset. Hence, the novel LiDAR bathymetric method can effectively achieve high-accuracy near-shore bathymetry. [ABSTRACT FROM AUTHOR]

Vehicular non-exhaust emissions account for a significant share of atmospheric particulate matter (PM) pollution, but few studies have successfully quantified the contribution of non-exhaust emissions via real-world measurements. Here, we conduct a comprehensive study combining tunnel measurements, laboratory dynamometer and resuspension experiments, and chemical mass balance modeling to obtain source profiles, real-world emission factors (EFs), and inventories of vehicular non-exhaust PM emissions in Chinese megacities. The average vehicular PM 2.5 and PM 10 EFs measured in the four tunnels in four megacities (i.e., Beijing, Tianjin, Zhengzhou, and Qingdao) range from 8.8 to 16.0 mg km−1 veh−1 and from 37.4 to 63.9 mg km−1 veh−1, respectively. A two-step source apportionment is performed with the information of key tracers and localized profiles of each exhaust and non-exhaust source. Results show that the reconstructed PM 10 emissions embody 51–64% soil and cement dust, 26–40% tailpipe exhaust, 7–9% tire wear, and 1–3% brake wear, while PM 2.5 emissions are mainly composed of 59–80% tailpipe exhaust, 11–31% soil and cement dust, 4–10% tire wear, and 1–5% brake wear. Fleet composition, road gradient, and pavement roughness are essential factors in determining on-road non-exhaust emissions. Based on the EFs and the results of source apportionment, we estimate that the road dust, tire wear, and brake wear emit 8.1, 2.5, and 0.8 Gg year−1 PM 2.5 in China, respectively. Our study highlights the importance of non-exhaust emissions in China, which is essential to assess their impacts on air quality, human health, and climate and formulating effective controlling measures. Image 1 • Six traffic-related sources were resolved and quantified via tunnel measurements. • Non-exhaust dominate PM 10 emission and significantly contribute to PM 2.5 emission. • Deposited tire wear particles accounted for a considerable share of road dust. • Fleet composition, pavement gradient and roughness result in emission variability. [ABSTRACT FROM AUTHOR]

Ships are considered important contributors to air pollution and have seriously health impact globally. Although many researchers have carried out studies on emission factors (EFs) in recent years, studies covering various types of ships are rare and the chemical characteristics of particulate matter (PM) emitted by them are unclear. Due to the diversity of different national fleets, the current emission inventory in China established by citing foreign EFs will lead to inaccurate calculation results and generate large uncertainty. In this study, the EFs for ocean-going, harbour, offshore and inland vessels were measured by the portable emission measurement system (PEMS). The specific compositions of PM were analyzed, including carbonaceous components, water-soluble ions (WSI), elements, and polycyclic aromatic hydrocarbons (PAHs). Combined with localized EFs, the ship emission inventory of Qingdao Port was established based on Automatic Identification System (AIS) data. The results show nitrogen oxide (NOx) is the primary pollutant discharged by all ships, followed by sulfur dioxide (SO 2). The emissions of SO 2 and PM from inland and offshore vessels fueled with high sulfur fuel (HSF) are significantly higher than other ships. Carbonaceous components accounted for 43.4%–53.9% of the total mass of PM, WSI accounted for 9.8%–14.3%, and elements accounted for 16.5%–28.6%. Although the use of low sulfur fuel (LSF) has reduced the emission of V and Ni from ships, it could still be treated as tracer elements of ship emissions. The annual emissions of SO 2 , NO X , PM 2.5 , THC and CO from ships in Qingdao port in 2020 were 9728.5, 38503.9, 1424.9, 1331.5 and 2440.4 tons, respectively. The ship emission inventory established based on EPA-recommended EFs resulted in underestimating approximately 16.8% of ship pollutants, especially for SO 2 and PM. Ocean-going ships and offshore vessels are major emitters in the study waters. This study is hoped to provide a valuable reference for the further regulation and policy formulation of ship emissions in China. [Display omitted] • On-board measurements were carried out on typical ships by PEMS. • EFs for gas pollutants and particle components of various vessel types are provided. • Fuel types have a significant impact on the particulate fraction of ships. • It reveals the current emissions from Chinese ships may be underestimated. • Approximately 16.8% of ship emissions in Qingdao Port are undervalued in 2020. [ABSTRACT FROM AUTHOR]

As the largest container port in northern China, the prosperous maritime trade has not only promoted the economic development of Qingdao port but also brought serious pollution to the air quality. Ship emissions have caused a significant influence on public health. The establishment of the Domestic Emission Control Area (DECA) is a key policy to reduce ship emissions in recent years. It has been implemented in Qingdao Port from January 1, 2019. To evaluate the environmental benefits of the DECA policy, this study established a bottom-up ship emission inventory in 2020 for Qingdao based on Automatic Identification System (AIS) data. Changes in ship emissions and their impact on air quality in Qingdao are evaluated for the same study area before and after the implementation of DECA policy. The total emissions in all study area for SO 2 , NO X , PM 10 , PM 2.5 , HC and CO in 2020 are 8.16 × 103, 3.31 × 104, 1.25 × 103, 1.15 × 103, 1.18 × 103, 2.15 × 103 tons (t), respectively, reductions of 67.9%, 15.4%, 63.2%, 62.9%, 27.2% and 23.2% compared with 2016, and reductions of 73.42%, 29.97%, 69.54%, 69.26%, 39.71%, 36.44% compared with the scenario without DECA in 2020. The inter-month change and spatial distribution of ship emissions are similar in both years. Shinan District, Shibei District and Huangdao District are most affected by ship emissions, with the largest influence in summer. The ship's contribution to air pollution in each area had been significantly reduced, especially for SO 2 and PM after DECA policy. However, the emission reduction of other pollutants is limited, so it is necessary to further enhance the DECA policy. Our research aims to conduct a comprehensive assessment of the environmental benefits for DECA policy, and provide support for the further improvement of the subsequent measures. [Display omitted] • High spatiotemporal resolution ship emission inventory for Qingdao in 2020 is updated. • Total ship emission in Qingdao during 2020 was 46,965.7 tons, 37.7% lower than 2016. • More than 50% of ship pollutants occur within the baseline of territorial sea. • Ship emissions in summer have the most significant impact on urban air quality. • DECA policy has reduced ship emission of SO 2 by 73.4% and PM 2.5 by 69.5%. [ABSTRACT FROM AUTHOR]