Perovskites with nano-flexible texture structures and excellent catalytic properties have attracted considerable attention for persulfate activation in addressing the organic pollutants in water. In this study, highly crystalline nano-sized LaFeO₃ was synthesized by a non-aqueous benzyl alcohol (BA) route. Under optimal conditions, an 83.9% tetracycline (TC) degradation and 54.3% mineralization were achieved at 120 min by using a coupled persulfate/photocatalytic process. Especially compared to LaFeO₃-CA (synthesized by a citric acid complexation route), the pseudo-first-order reaction rate constant increased by 1.8 times. We attribute this good degradation performance to the highly specific surface area and small crystallite size of the obtained materials. In this study, we also investigated the effects of some key reaction parameters. Then, the catalyst stability and toxicity tests were also discussed. The surface sulfate radicals were identified as the major reactive species during the oxidation process. This study provided a new insight into nano-constructing a novel perovskite catalyst for the removal of tetracycline in water.

Limiting the fuel sulphur content (FSC) of ships can significantly reduce the harm caused by ship emissions, and analyzing ship exhaust gas to estimate FSC is a rapid, efficient, and low-cost monitoring method. To solve the difficulty in measuring FSC using sniffer equipment in a complex port area, a ship emission monitoring option for FSC measurement in complex environments is proposed here. First, the exhaust gas measurement data of a time series collected using the sniffer equipment were examined to determine the dataset that could be used to estimate FSC. Second, the background value of polluted gases in the environment was dynamically calculated to suppress the interference of various pollution sources. The gas-measured value series was then converted into a mean value series, and the peak points in the mean value series were automatically selected for the calculation of FSC. Finally, the wind speed, wind direction, automatic identification system information, plume diffusion model, and FSC results of ship targets around the equipment were correlated. Between June and August 2023, we conducted a field observation campaign at Ningbo Port, China, where 2624 ships were monitored. A comparison of the real and measured FSC values of eight ships showed that the system could accurately measure FSC at 0.10% (m/m) and 0.50% (m/m) levels despite measurement uncertainty that may be greater at a 0.01% (m/m) FSC level. The FSC statistics of 2624 ships showed that the FSC of small seagoing ships was relatively higher than that of other types of ships. This study proposes a monitoring option for ship emissions, designs and develops an associated system, and collects data to validate the effectiveness and accuracy of this option. This approach has been integrated into daily business operations within the maritime sector, significantly enhancing the efficiency of supervision in this field.

Inland river ships (IRSs) use diesel with a lower sulfur content and emit relatively low emissions, making it challenging to monitor their emissions. Sniffer monitoring equipment was installed from August 2020 to June 2022 at the Gezhou Dam of the Yangtze River and monitored emissions from 8,238 IRSs passing through the lock. We partnered with the maritime department to select 100 ships passing through the lock to extract fuel oil samples for direct fuel sulfur content (FSC) detection, which determined the actual FSC of the passing ships. The monitoring data from these 100 ships indicated that the relative error of the SO₂ emission factors (EFs) and FSC results is significant at the 10-parts-per-million level. The monitoring data from the remaining 8,138 ships showed that the EFs of NO, NO₂, PM_2.5, and PM₁₀ were 24.02 ± 16.92 g kg⁻¹, 10.30 ± 18.08 g kg⁻¹, 0.72 ± 0.60 g kg⁻¹, and 0.92 ± 0.70 g kg⁻¹, respectively. The NOx EFs of container ships are higher than those of other ship types, while the PM EFs for different ship types do not significantly differ. Based on these EFs, we calculated the average emission rates for different types of ships passing through locks, which is a real-time measurement method for estimating ship emissions. In addition, a comparison of ship EF measurements over the past 20 years revealed that EF values for SO₂, NOx, and PM exhibited a downward trend, with the calculated results of the current study determined to be the lowest numerical level.

During the waiting period before passing through locks, inland vessels typically rely on diesel generators to power their onboard equipment, which leads to air pollution and poses more direct threats to the surrounding residents and ecological environment. To assess the extent to which green and efficient lock passage strategies can reduce air pollution, this study takes the Linked Control Policy for Vessels Passing Through Locks released by the Three Gorges Navigation Authority in China in December 2017 as the research object. It collected air quality monitoring data for six years before and after the policy implementation (2014–2020) and used a Regression Discontinuity model (RD model) to analyze the policy’s effect. The results show that compared to 2014, the average concentration of SO2 in the air decreased by 67% in 2020, along with NO2 decreasing by 32%, PM2.5 by 42%, PM10 by 46%, and the AQI (Air Quality Index) by 27%. The robustness test of the RD model also confirmed the causal relationship between the policy implementation and the improvement in air quality. This research is the first to systematically disclose the environmental benefits of the “soft” management policy of optimizing the lock passage process, uncovering the positive influence of active ship lock passage policy on air quality, and providing a scientific basis for promoting the implementation of policies related to lock management.

Multimodal sensing provides a mature foundation for elevator monitoring, while visual records, equipment-state records, and operational responses still need to be connected in many deployed community systems. This study develops an event-level closed-loop governance framework for that connection. Continuous visual detections and abnormal equipment states are aggregated by elevator identifier and time to form candidate events with explicit temporal boundaries. Rule-based event-level integration assigns each candidate a platform-confirmed, pending-verification, or excluded state. An event-triggered supervisory loop then maps event state, type, persistence, and potential consequence to monitoring, verification, voice prompting, authorized door-control linkage, work-order dispatch, or emergency escalation; updated equipment, work-order, and on-site evidence determine closure, continued tracking, or escalation. A retrospective evaluation used operational data from October 2024 to March 2025 for a community smart-elevator platform in Shanghai’s Lingang New Area. Among 3657 platform-confirmed risk events, 1754 were faults, 1814 were anomalies, and 89 were passenger-entrapment events, equivalent to 279.4 events per 100 deployed elevators over six months. Ten communities accounted for 57.2% of all events. Scenario, Criteria Importance Through Intercriteria Correlation (CRITIC), minimum-value, weight-perturbation, and screening-threshold checks were used to examine the stability of maintenance priorities. Community A remained the highest-priority community in all comparisons. The three attention bands were further mapped to priority intervention, scheduled targeted inspection, and routine preventive monitoring strategies. This mapping prioritizes limited inspection and diagnostic capacity; it is not an estimate of component remaining useful life. The framework therefore connects risk recognition, supervisory response, recovery verification, and evidence-based maintenance prioritization without extending the available data beyond their operational scope.