N-containing dissolved organic matter promotes dissolved inorganic carbon supersaturation in the Yangtze River, China.

Dissolved inorganic carbon (DIC) represents a major global carbon pool and the flux from rivers to oceans has been observed to be increasing. The effect of weathering with respect to increasing DIC has been widely studied in recent decades; however, the influence of dissolved organic matter (DOM) on increasing DIC in large rivers remains unclear. This study employed stable carbon isotopes and Fourier transform ion cyclotron mass spectrometry (FT-ICR MS) to investigate the effect of the molecular composition of DOM on the DIC in the Yangtze River. The results showed that organic matter is an important source of DIC in the Yangtze River, accounting for 40.0 ± 12.1 % and 32.0 ± 7.2 % of DIC in wet and dry seasons, respectively, and increased along the river by approximately three times. Nitrogen (N)-containing DOM, an important composition in DOM with a percentage of ∼40 %, showed superior oxidation state than non N-containing DOM, suggesting that the presence of N could improve the degradable potential of DOM. Positive relationship between organic sourced DIC (DICOC) and N-containing DOM formulae indicated that N-containing DOM is crucial to facilitate the mineralization of DOM to DICOC. N-containg molecular formular with low H/C and O/C ratio were positively correlated with DICOC further verified these energy-rich and biolabile compounds are preferentially decomposed by bacteria to produce DIC. N-containing components significantly accelerated the degradation of DOM to DICOC, which is important for understanding the CO2 emission and carbon cycling in large rivers.

Anthropogenic Activities Generate High-Refractory Black Carbon along the Yangtze River Continuum.

Combustion-driven particulate black carbon (PBC) is a crucial slow-cycling pool in the organic carbon flux from rivers to oceans. Since the refractoriness of PBC stems from the association of non-homologous char and soot, the composition and source of char and soot must be considered when investigating riverine PBC. Samples along the Yangtze River continuum during different hydrological periods were collected in this study to investigate the association and asynchronous combustion drive of char and soot in PBC. The results revealed that PBC in the Yangtze River, with higher refractory nature, accounts for 13.73 ± 6.89% of particulate organic carbon, and soot occupies 37.53 ± 11.00% of PBC. The preponderant contribution of fossil fuel combustion to soot (92.57 ± 3.20%) compared to char (27.55 ± 5.92%), suggested that fossil fuel combustion is a crucial driver for PBC with high soot percentage. Redundancy analysis and structural equation modeling confirmed that the fossil fuel energy used by anthropogenic activities promoting soot is the crucial reason for high-refractory PBC. We estimated that the Yangtze River transported 0.15-0.23 Tg of soot and 0.15-0.25 Tg of char to the ocean annually, and the export of large higher refractory PBC to the ocean can form a long-term sink and prolong the residence time of terrigenous carbon.

Mitigation of China's carbon neutrality to global warming.

Projecting mitigations of carbon neutrality from individual countries in relation to future global warming is of great importance for depicting national climate responsibility but is poorly quantified. Here, we show that China's carbon neutrality (CNCN) can individually mitigate global warming by 0.48 °C and 0.40 °C, which account for 14% and 9% of the global warming over the long term under the shared socioeconomic pathway (SSP) 3-7.0 and 5-8.5 scenarios, respectively. Further incorporating changes in CH4 and N2O emissions in association with CNCN together will alleviate global warming by 0.21 °C and 0.32 °C for SSP1-2.6 and SSP2-4.5 over the long term, and even by 0.18 °C for SSP2-4.5 over the mid-term, but no significant impacts are shown for all SSPs in the near term. Divergent responses in alleviated warming are seen at regional scales. The results provide a useful reference for the global stocktake, which assesses the collective progress towards the climate goals of the Paris Agreement.

Evaluating the impact of roads on the diversity pattern and density of trees to improve the conservation of species.

Roadside trees alter biotic and abiotic factors of plants diversity in an ecosystem. Rows of plants grow along the roadside due to the interplay between the arrival of propagule and seedling establishment, which depends on the road's specifications, land pattern, and road administration and protection practices. A field study was conducted to measure the roadside tree diversity in the city of Karachi (Pakistan). A total of 180 plots, divided into three primary road groups, were surveyed. The highest quantity of tree biomass per unit area was found on wide roads, followed by medium roads. On narrow roads, the least biomass was detected. A single species or a limited number of species dominated the tree community. Conocarpus erectus was the most dominant non-native species on all types of sidewalks or roadsides, followed by Guaiacum officinale. A total of 76 species (32 non-natives and 44 natives) that were selectively spread along the roadsides of the city were studied. There was a significant difference in phylogenetic diversity (PD), phylogenetic mean pairwise distance (MPD), and phylogenetic mean nearest taxon distance (MNTD) among wide, medium, and narrow roads. Management practices have a significant positive correlation with diversity indices. Our study identified patterns of diversity in roadside trees in Karachi. It provides the basis for future planning for plant protection, such as the protection of plant species, the maintenance of plant habitats, and the coordination of plant management in Karachi.

Assessing the change of ambient air quality patterns in Jiangsu Province of China pre-to post-COVID-19.

Following the outbreak of the novel coronavirus in early 2020, to effectively prevent the spread of the disease, major cities across China suspended work and production. While the rest of the world struggles to control COVID-19, China has managed to control the pandemic rapidly and effectively with strong lockdown policies. This study investigates the change in air pollution (focusing on the air quality index (AQI), six ambient air pollutants nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), carbon monoxide (CO), particulate matter with aerodynamic diameters ≤10 µm (PM10) and ≤2.5 µm (PM2.5)) patterns for three periods: pre-COVID (from 1 January to May 30, 2019), active COVID (from 1 January to May 30, 2020) and post-COVID (from 1 January to May 30, 2021) in the Jiangsu province of China. Our findings reveal that the change in air pollution from pre-COVID to active COVID was greater than in previous years due to the government's lockdown policies. Post-COVID, air pollutant concentration is increasing. Mean change PM2.5 from pre-COVID to active COVID decreased by 18%; post-COVID it has only decreased by 2%. PM10 decreased by 19% from pre-COVID to active COVID, but post-COVID pollutant concentration has seen a 23% increase. Air pollutants show a positive correlation with COVID-19 cases among which PM2.5, PM10 and NO2 show a strong correlation during active COVID-19 cases. Metrological factors such as minimum temperature, average temperature and humidity show a positive correlation with COVID-19 cases while maximum temperature, wind speed and air pressure show no strong positive correlation. Although the COVID-19 pandemic had numerous negative effects on human health and the global economy, the reduction in air pollution and significant improvement in ambient air quality likely had substantial short-term health benefits; the government must implement policies to control post-COVID environmental issues.

Query the trajectory based on the precise track: a Bloom filter-based approach.

Fast and precise querying in a given set of trajectory points is an important issue of trajectory query. Typically, there are massive trajectory data in the database, yet the query sets only have a few points, which is a challenge for the superior performance of trajectory querying. The current trajectory query methods commonly use the tree-based index structure and the signature-based method to classify, simplify, and filter the trajectory to improve the performance. However, the unstructured essence and the spatiotemporal heterogeneity of the trajectory-sequence lead these methods to a high degree of spatial overlap, frequent I/O, and high memory occupation. Thus, they are not suitable for the time-critical tasks of trajectory big data. In this paper, a query method of trajectory is developed on the Bloom Filter. Based on the gridded space and geocoding, the spatial trajectory sequences (tracks) query is transformed into the query of the text string. The geospace was regularly divided by the geographic grid, and each cell was assigned an independent geocode, converting the high-dimensional irregular space trajectory query into a one-dimensional string query. The point in each cell is regarded as a signature, which forms a mapping to the bit-array of the Bloom Filter. This conversion effectively eliminates the high degree of overlap and instability of query performance. Meanwhile, the independent coding ensures the uniqueness of the whole tracks. In this method, there is no need for additional I/O on the raw trajectory data when the track is queried. Compared to the original data, the memory occupied by this method is negligible. Based on Beijing Taxi and Shenzhen bus trajectory data, an experiment using this method was constructed, and random queries under a variety of conditions boundaries were constructed. The results verified that the performance and stability of our method, compared to R*tree index, have been improved by 2000 to 4000 times, based on one million to tens of millions of trajectory data. And the Bloom Filter-based query method is hardly affected by grid size, original data size, and length of tracks. With such a time advantage, our method is suitable for time-critical spatial computation tasks, such as anti-terrorism, public safety, epidemic prevention, and control, etc.

Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach.

Passive infrared (PIR) motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA)-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks.