Majority Of Stations Research Articles

Spatial distribution, ecological and ecotoxicity evaluation of heavy metals in agricultural soils along Lala-Manjo Highway, Cameroon Volcanic Line

This study aims at assessing the spatial distribution, ecological and toxicity levels of heavy metals in agricultural soils of Lala-Manjo Highway in Cameroon. The soil samples were collected and analyzed by inductively coupled plasma-mass spectrometry. The result shows that Co, Cr, Cu, Hg, Mn, Ni, Sr, V and Zn concentrations are higher than those of the upper continental crust reference values except those of As, Cd and Pb. Correlation coefficient indicate similar input sources governed by same geochemical characteristics. Spatial distribution maps show high concentration of heavy metals in east, south and western part of the study area. Pollution indicators show that enrichment factor varied from moderate to high enrichment of heavy metals. The contamination degree (27.12–35.59), geo-accumulation index (0.01–4.94), and potential ecological risk index (RI = < 150) revealed a low to moderate levels of metallic pollution. The majority of stations have pollution load index and Nemerow integrated pollution index values greater than one, indicating serious pollution. Modified hazard quotient and toxic risk index suggest significant severity of pollution and low toxicity risk of heavy metals, pointing low toxicity effects of heavy metals to fauna and flora in the soil ecosystem. Considering the factors governing soils accumulation such as geology, climate and anthropogenic activities as well as the correlation with commonly used environmental background, it is proposed that the data from this study can be considered as a baseline of pollution to plan major agricultural developments in the area. The study underlines the importance of continuous monitoring and adaptive management for the preservation of soil ecosystem. The results may assist companies and local population in setting realistic targets for monitoring and remediation programs.

Precipitation in Kathmandu Valley: A Spatial-temporal Study

Over the past decade, advancements in atmospheric research have significantly improved the analysis of precipitation monitoring, understanding climate trends and variability, and identifying climatic anomalies associated with dry and wet periods. A key objective of this research is to enhance the understanding of precipitation patterns in the Kathmandu Valley, particularly in relation to the placement of gauges for long-range predictions and subsequent forecasting challenges. This study focuses on the evolving patterns of precipitation across the three districts within the Kathmandu Valley, the most developed and densely populated area in Nepal, encircled by four mountain ranges. We employ interpolation methods in ArcGIS Pro to identify, estimate, and forecast precipitation trends, and use the Mann-Kendall test to reveal statistically significant trends and provide insights into broader climatic processes. The research analyzes trends in both seasonal and annual precipitation and temperature across four time periods—pre-monsoon, monsoon, post-monsoon, and winter—using the Mann-Kendall test. The analysis reveals a decreasing trend in monthly rainfall from 2003 to 2017 at the majority of stations (14 stations out of 19), with areas along the valley slopes receiving significantly more precipitation compared to the central valley. The results of this study contribute to a deeper understanding of climate change in the Kathmandu Valley.

Innovative trend analysis technique with fuzzy logic and K-means clustering approach for identification of homogenous rainfall region: A long-term rainfall data analysis over Bangladesh

Understanding regional climatic trends is crucial for taking appropriate actions to mitigate the impacts of climate change and managing water resources effectively. This study aims to investigate the dissimilarities and similarities among various climate stations in Bangladesh from 1981 to 2021. Fuzzy C-means (FCM) and K-means clustering techniques were employed to identify regions with comparable rainfall patterns. Moreover, the innovative trend analysis (ITA) and the Mann-Kendall (MK) test family were utilized to analyze rainfall trends. The results indicate that both K-means and FCM methods successfully detected two rainfall regions in Bangladesh with distinct patterns. The ITA curve analysis revealed that out of the 29 stations, 13 had a non-monotonic increasing trend having no monotonic increasing trend, 8 had a non-monotonic decreasing trend, and 8 exhibited a monotonic decreasing trend. Additionally, the MK tests employed in the study showed predominantly negative trends across Bangladesh. The majority of stations (65.51%) fell into Cluster 1, while the remaining 34.48% were in Cluster 2. In terms of ITA analysis, 17.24% of stations exhibited a monotonic decrease, while there were no stations with a monotonic increase. However, 37.93% of stations showed a non-monotonic increase, and 44.83% displayed a non-monotonic decrease. These identified regions can provide valuable insights for water resource management, disaster risk reduction, and agricultural planning. Moreover, detailed rainfall analysis can help policymakers and scientists develop sustainable and effective regional-scale policies for managing the country's flood and drought situations, ultimately supporting agricultural development and environmental planning.

Assessment of heavy metals and radionuclides (U and Th) contamination, and source identification of the Lékié Watershed sediments, Cameroon

This study aims to assess the pollution levels and sources of heavy metals (Al, Mn, Co, Ni, Sc and V) and radionuclides (Th and U) in sediments of Lékié Watershed in Cameroon. Surface sediment samples were collected from 14 stations across different areas of the Lékié Watershed. The heavy metal and radionuclide analysis on sediments were carried out and pollution indices such as enrichment factor (EF), contamination factor (CF), degree of contamination (DC), pollution load index (PLI), potential ecological risk assessment and multivariate statistical methods were utilized to evaluate the degree of contamination and estimate the source of metals in the sediments of Lékié Watershed. The results show that the mean concentrations are lower than those of the upper continental crust (UCC) reference values except those of Mn, Co, Ni, and V. The values of EF varied from moderate to severe enrichments (from 4 to 44). The contamination factor (1 < CF < 3), potential ecological risk (<40) and degree of contamination (<8) revealed a low to moderate degree of contamination. The majority of stations have PLI > 1, indicating pollution. The application of multivariate analysis to these metallic elements reveals contaminations attributed to anthropogenic activities, including the release of domestic, artisanal mining, and industrial effluents, as well as natural sources originating from the leaching of local surrounding soils and geological factors. To mitigate more pollution in this watershed which is closed from urbanization, it is crucial for local authorities to implement wastewater treatment measures.

Statistical and Spatial Analysis of Extreme Temperatures in Libya (1961-2020)

The present paper seeks to examine spatial and temporal variability of changes in both warm and cold extreme temperatures. Additionally, it aims to determine the most extreme geographical areas in extreme temperatures based on climate data recorded during the period from 1961 to 2020. This paper analysed the annual data of extreme temperatures for 16 meteorological stations during 1961- 2020 across Libya over two climatic regions (coastal and inland) based on Mann–Kendall test. Time trend of the extreme of both maximum and minimum temperature will be analyses and the results from geographical analysis are will also be presented based on ArcMap. The results indicated that a significant increase in the coldest extreme (TNn) temperature are found in autumn and spring and winter at majority of stations. Temperature variability can be associated with variations in large-scale atmospheric patterns represented by Eastern Atlantic and the Western Mediterranean Oscillations.

Biometeorological conditions during cold spells in south-east Poland and west Ukraine.

The aim of this research is to analyze the biometeorological conditions, based on the Physiologically Equivalent Temperature (PET) thermal index, during cold spells (CSs) in south-east Poland and west Ukraine during the years 1966-2021. The research shows a high variability of the occurrence of CSs in the study period and a clear increase in the frequency and total duration of CSs in the east of the study area. The number of CSs in the analyzed years varies from 6 cases in the west (in Katowice) to 34 in the east of the study area (in Shepetivka). The total duration of CSs varied from 26 days (in Raciborz and Katowice) to 166 days (in Rivne). At the majority of stations, CSs occurred most frequently in the first two decades (1966/1967-1975/1976, 1976/1977-1985/986) and in the last full decade (2006/2007-2015/2016). The average PET values at 12:00 UTC during CSs decreased eastwards throughout the study domain and were generally lower than -20.0 °C in the west of Ukraine, while in south-east Poland varied between -18.1 and -20.0 °C. At 40% of stations across the study domain, the lowest average PET values were recorded during a cold spell in January 1987, with PET values varying from -28.0 °C in Chernivtsi to -12.7 °C in Yaremche. The longest or one of the longest spells in most stations (in 77% of stations across the study domain) was the cold spell of 2012 and characterized by mean PET values ranging from -25.4 °C in Rivne to -19.5 °C in Zakopane.

Distribution, Transport, and Impact on Air Quality of Two Typical Dust Events in China in 2021

The dust event from 12 January to 17 January 2021 (“1.12 event”) is the first dust process in 2021 and the earliest dust event in the last two decades. The dust event from 14 to 18 March 2021 (“3.15 event”) was the strongest dust storm in the past decade. Distribution, transport, and impact on urban air quality of these two typical dust events were studied using multi-source satellite data, a HYSPLIT trajectory model, and a 3D concentration-weighted trajectory model. Results show that both dust events affected a wide range of areas, covering Northwest, North, Northeast, East, and Central-South China. A strong dust belt spanning Northwest, North, and Northeast China was formed in northern China on 15 March 2021. The distribution heights of the 1.12 and 3.15 events were 0–5 km and 0–10 km, respectively. Dust from western Inner Mongolia and southern Mongolia dominated the 1.12 event, while dust from southern Mongolia dominated the 3.15 event. Both of these dust sources had eastward and southeastward transport paths. The majority of the dust was near-ground in downstream cities from an altitude of 0–3 km. Most cities were affected by the dust backflow. The 1.12 event generated more severe particulate pollution in southern China than the 3.15 event. During high-value dust days, ozone pollution levels decreased at the majority of stations. Dust weather with low dust rising heights and dust backflow phenomena should be taken seriously in urban dust pollution forecasting and warning work. International collaboration is needed to improve China’s desertification control.

Analysis of changes in extreme rainfall characteristics over Ambica river basin, India

ABSTRACT Significant variations are detected in extreme rainfall characteristics over Ambica river basin, Gujarat. The frequent flooding in Ambica basin has become a persistent phenomenon over last decade. Based on new knowledge and understanding in Extreme Value Theory (EVT), the discrepancies in extreme rainfall characteristics over entire basin are analyzed considering the daily rainfall series of 25 rain gauge stations for period of 1961–2017. At each station, the threshold value was selected as 90th percentile rainfall for computing the excesses of Peak over Threshold (POT) series. To examine the variations in rainfall pattern, basic rainfall indices viz. total annual rainfall (PRCPTOT), rainy days (RD) and simple daily intensity index (SDII) are calculated. To assess the non-uniformity of extreme rainfall, coefficient of variation (Cv) has been found. Non-parametric tests like Modified Mann-Kendall (MMK) test and Sen’s slope test were used for evaluating the trend. The western parts of the basin show a declining trend in PRCPTOT and SDII, wherein stationarity has been observed for RD. The stations in the central region show a significantly declining trend in total annual rainfall. The SDII and RD are found to be higher and lower respectively for the stations situated in eastern parts of the basin. It has been found that 20 %, 4 % and 17 % of stations exhibit significant decreasing trends in PRCPTOT, RD and SDII respectively. The western basin has lesser values of coefficient of variation (Cv) as compared to stations in eastern part. At the majority of stations, extreme rainfall duration and frequency were found to be stationary, whereas intensity of extreme rainfall is found to be non-stationary. Also, it has been found that 16 % of stations exhibit significant decreasing trends in extreme rainfall intensity and duration, which demonstrates the consequences to water availability in near future.

Three-Dimensional Dynamic Variations of Ground/Air Surface Temperatures and Their Correlation with Large-Scale Circulation Indexes in Southwest China (1980–2019)

Air/soil temperatures play important roles in land–atmosphere interactions. The three-dimensional (temporal, spatial, and vertical) variations of maximum, mean, and minimum ground soil temperature at 0 cm (GSTx, GSTm, and GSTn, respectively), surface air temperature at 2 m (SATx, SATm, and SATn, respectively), and soil–air temperature difference (SATDx, SATDm, and SATDn, respectively) and their potential linkages with large-scale indexes in Southwest China during 1980–2019 were analyzed. Variations of GST and SAT at the majority of stations (pixels) exhibited significant (p ≤ 0.05) warming, albeit at different rates; consequently, SATD exhibited different variation. Moreover, the period of GST, SAT, and SATD was similar in intra-annual and interannual oscillation but was different in interdecadal oscillation. The variation rate of GST, SAT, and SATD exhibited significant (p ≤ 0.05) correlation with elevation, but with different variation gradient. Notably, asymmetric variation of SATDx (downward trend) and of SATDn (upward trend) with elevation was found at elevations &gt;3 km. Wavelet coherence showed that the Atlantic Multidecadal Oscillation is the dominant factor affecting GST and SAT, whereas the Pacific Decadal Oscillation and the North Atlantic Oscillation make the greatest contributions to SATD. It was found that GST, SAT, and SATD exhibit different variations under the effects of global warming, the driving mechanism of which requires further study.

The periodicity and initial evolution of micro-mobility systems: a case study of the docked bike-sharing system in New York City, USA

ObjectivesThis study developed an analytical framework that aims at understanding the evolutionary processes of a micro-mobility system (for example, bike-sharing), which offers insights into the transforming nature of a city transport system.MethodsFirstly, the framework applied a Gaussian Mixture Model to examine the long-term fluctuations of travel demands. Secondly, it investigated the growth trajectories of service points via exponential and logistic growth models. Cumulative connections with other points represented the growth of a service location. An eigendecomposition approach was used to uncover the hidden structures behind the growth curves.ResultsThis framework was applied in the docked bike-sharing program in New York City, USA. The results show that there existed periodic patterns of travel demands in the long term. The majority of stations grew rapidly after they began to operate. However, the temporal signatures of stations’ growth displayed some variations across different locations.ConclusionThis proposed workflow can be employed in other cities with similar context to better investigate how micro-mobility systems evolve.

Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System

Abstract. Streamflow forecasts provide vital information to aid emergency response preparedness and disaster risk reduction. Medium-range forecasts are created by forcing a hydrological model with output from numerical weather prediction systems. Uncertainties are unavoidably introduced throughout the system and can reduce the skill of the streamflow forecasts. Post-processing is a method used to quantify and reduce the overall uncertainties in order to improve the usefulness of the forecasts. The post-processing method that is used within the operational European Flood Awareness System is based on the model conditional processor and the ensemble model output statistics method. Using 2 years of reforecasts with daily timesteps, this method is evaluated for 522 stations across Europe. Post-processing was found to increase the skill of the forecasts at the majority of stations in terms of both the accuracy of the forecast median and the reliability of the forecast probability distribution. This improvement is seen at all lead times (up to 15 d) but is largest at short lead times. The greatest improvement was seen in low-lying, large catchments with long response times, whereas for catchments at high elevation and with very short response times the forecasts often failed to capture the magnitude of peak flows. Additionally, the quality and length of the observational time series used in the offline calibration of the method were found to be important. This evaluation of the post-processing method, and specifically the new information provided on characteristics that affect the performance of the method, will aid end users in making more informed decisions. It also highlights the potential issues that may be encountered when developing new post-processing methods.

Statistical modeling of annual highest monthly rainfall in Zimbabwe

The first statistical analysis of maximum rainfall in Zimbabwe is provided. The data are from 103 stations spread across the different climatic regions of Zimbabwe. More than 90% of the stations had at least 50 years of data. The generalized extreme value distribution was fitted to maximum rainfall by the method of maximum likelihood. Probability plots, quantile plots and Kolmogorov–Smirnov tests showed that the generalized extreme value distribution provided an adequate fit for all stations. The vast majority of stations do not exhibit significant trends in rainfall. Twelve of the stations exhibit negative trends and three of the stations exhibit positive trends in rainfall. Estimates of return levels are given for 2, 5, 10, 20, 50 and 100 years.

High-frequency sea-level analysis: Global distributions

This paper provides global climatology of high-frequency (T < 2 h) sea-level oscillations of atmospheric origin. Sea-level series with a temporal resolution of 1 min from 331 tide gauges in the world ocean and spanning between 1.5 and 12 years were analysed to determine the typical ranges, seasons and characteristics of moderate and extreme manifestations of nonseismic sea-level oscillations at tsunami timescales (NSLOTTs). Variances in these oscillations reach 6.6 cm2 on average, but their region-averaged extreme (99.99th percentile) ranges may exceed half a metre or more at individual hotspot locations. The NSLOTTs are the highest in the mid-latitudes and decrease towards polar and tropical regions. Moderate oscillations show clear winter maxima at the majority of stations, while a less pronounced pattern is evident for the most extreme oscillations, which may occur throughout a year. A total of 3080 of the most prominent NSLOTT events were selected from 308 stations and compared to background oscillations, revealing the substantial capability of energy amplification during extreme events. Synchronicity in extreme NSLOTT events between neighbouring stations (up to 250 km) in mid-latitudes has been detected for 20–32% of all episodes, suggesting possible connectivity between the episodes.

Analysis of Rainfall Variability and Trends Over Nzoia River Basin, Kenya

Nzoia river is mainly rain fed and the basin is one of the regions that is highly vulnerable to climate change in Kenya. Understanding rainfall variability and trends is important for better water resources management and economic development in the basin. The aim of this study is to assess variability and trends in rainfall at 13 sites within Nzoia River Basin over the period, 1970 to 2001, using the parametric test of Linear regression analysis and the non-parametric Mann–Kendall statistical test. Data for this study was obtained from the Kenya Meteorological Department (KMD). The basin experiences four rainfall seasons in a year as a result of the Inter-Tropical Convergence Zone (ITCZ). There are two rainy seasons and two dry seasons. Annual rainfall through Linear regression analysis shows 6 stations, Kaimosi Tea Estate Ltd, Kakamega Meteorological Station, Bungoma Water Supply, Nzoia Forest Station, Malava Forest Station and Webuye Agricultural Office with declining rainfalls. The remaining 7 stations, Leissa Farm Kitale, Turbo Forest Nursery, Chorlim ADC Farm, Kaptagat Forest Station, Kimilili Agricultural Department, Bunyala Irrigation Scheme and Kadenge Yala Swamp showed increasing rainfalls. The majority of stations with increasing annual rainfall are in the upper catchment whereas those with decreasing rainfall are in the middle and lower catchment. Only 3 out of the 13 stations showed statistically significant trends in rainfall with two in the upper catchment and one in the middle; the remaining 10 stations had statistically insignificant trends. These observed changes in rainfall, although most time series are not convincing as they show predominantly no significance, along with the reported climatic warming in most parts of the basin may have future implications on human health, water resources management, various plant and animal species bio-diversity and the overall economic development of the basin.

Variation of Hourly Extreme Precipitation in the Three Gorges Reservoir Region, China, from the Observation Record

Extreme hourly precipitation is amongst the most prominent driving factors of flash floods and geological disasters. Based on the hourly precipitation data of 35 stations in the Three Gorges Reservoir Region (TGRR) from 1998 to 2020, we analyzed the spatiotemporal variation characteristics of hourly extreme precipitation indexes. The selected indicators included the frequency, intensity, period, annual maximum, trend of hourly heavy precipitation (20–50 mm/h) and hourly extreme heavy precipitation (≥50 mm/h) in the TGRR. Closely related climatic factors such as the Western Pacific Subtropical High Intensity (WPSHI) were also discussed. The results showed that in 2010–2020, the cumulative frequency of heavy precipitation magnitude between 25 and 40 mm/h slightly increased, while the corresponding frequency for magnitudes ≥50 mm/h decreased. In summer, the frequency of both heavy and extreme heavy precipitation increased in June and decreased in August, indicating a shift of extreme events to an earlier time in the flood season. The cumulative frequency of heavy precipitation in July had a period of about 7a, and that of extreme heavy precipitation had a period of 3a. The annual average intensity of heavy precipitation and extreme heavy precipitation in the TGRR was 28.9 mm/h and 61.4 mm/h per station, respectively, and both fluctuated and insignificantly decreased from 1998 to 2020. The annual maximum hourly precipitation center in the TGRR moved downstream from west to northeast. The frequency of heavy precipitation was relatively small along the main stream of the river valley. Both the frequency and total amount of heavy precipitation in southeast of the TGRR were significantly higher than those in other regions. Heavy precipitation in the majority of stations with high elevation (higher than 500 m) showed a decreasing trend. The cumulative frequency of precipitation with an intensity of 20–50 mm/h was closely correlated with the Western Hemisphere Warm Pool (WHWP) Index in February and the WPSHI Index in January, and especially, the abnormal large annual frequency (top 20%) showed strong correlation with the two indexes, implying highly predictable factors for extreme events. The frequency of precipitation intensity above 50 mm/h was correlated with the Western Pacific Warm Pool (WPWP) Area Index in January and the WPWP Intensity Index in November of last year. The research results provide a strong and refined factual basis for the assessment and prediction of extreme precipitation, and for disaster prevention and mitigation, in the TGRR.

Evidence of shorter more extreme rainfalls and increased flood variability under climate change

Increases in extreme rainfall intensities as a result of climate change pose a great risk due to the possibility of increases in pluvial flooding, particularly in urban and developed areas. But evidence is emerging that the observed increases in extreme rainfall are not resulting in universal increases in flooding. Indeed, on a global scale, studies consistently find more gauge stations with decreasing rather than increasing trends in the annual maxima flood magnitude. Here, we aim to improve our understanding of how rainfall and streamflow extremes are changing and why floods are not always observed to increase despite increases in rainfall extremes. To do so, we examine trends in streamflow events using 2776 stations from the Global Runoff Data Centre, with events chosen to isolate the impact of changes to their respective rainfall and antecedent soil moisture. The analysis is limited to stations with 30 years or more of active record with the majority of stations in North America, Europe, Brazil, Oceania, and southern Africa.Consistent with physical reasoning, for more frequent events such as the annual maxima, it is found the peak 1-day rainfall event intensity is increasing approximately a rate of 6–7%/°C, with rarer event intensities increasing at a rate exceeding the Clausius-Clapeyron relation. We find that storm volumes are not increasing as greatly as the peak rainfall and storm durations are decreasing, pointing to an intensification of rainfall events and a peakier temporal pattern. While rainfall is intensifying, the magnitude of frequent floods (those expected to occur on average once per year) are in general decreasing, particularly in tropical and arid regions of the world. We find that this is likely due to a dominance of drying antecedent soil moisture conditions. However, the magnitude of rarer floods has, on average been increasing. We suggest this is because, for these rarer events, the increase in rainfall outweighs the decrease in soil moisture. Our results point to a worst of both world’s scenario where small floods, responsible for filling our water supplies, are decreasing, while the large flood events which pose a risk to life and infrastructure, are increasing.

Assessment of the effects of seasonal changes, urban discharges and leachates on the parasitological and bacteriological qualities of soil and water from Oued Larbaâ (North-eastern, Morocco)

The objective of this study is to evaluate the parasitological and bacteriological qualities of the soil and water of the Oued Larbaâ, through nine stations covering the part of the Oued crossing the Taza city of Morocco (7km of length). This study was conducted during two dry periods (2017 and 2018) and one wet period (2018). Soil parasitological studies revealed high contamination by helminths belonging to the species Enterobius vermicularis and Toxocara sp., which recorded the highest concentrations in stations S2 and S8, respectively, with values of 11.41 and 29.26 eggs/g. Concerning water, the majority of stations indicated contamination mainly by Ascaris lumbricoides, which reached concentrations of 56.49, 37.39, and 56.15 eggs/l in stations S3, S6, and S8, respectively. Bacteriological examination of the soil showed very high concentrations of bacteria, especially Streptococcus, due to the high retention of this type of bacteria in the soil. The water analysis indicated that stations S3, S4, and S8 are the most impacted, with total coliform and Streptococcus concentrations in station S3 of 2.45 105 and 0.20 107CFU/ml, respectively, Fecal coliforms and Staphylococci are detected in high concentrations in station S8 with values of 1.35 104 and 8.92 106CFU/ml, respectively, and total aerobic mesophilic flora in station S4 with a concentration of 1.89 109CFU/ml. During the study periods, influences of sampling season, nature of discharge existed (wastewater or leachate) on the parasitological and bacteriological composition of the river were established with high concentrations during the dry period and in the stations in which large amounts of liquid discharge were observed.

Temporal characterization of minute-level PM2.5 variation within a local monitoring network using DWT-DTW

Meticulously understanding the local processes of particulate pollution can help evaluate and mitigate its impact on residents. However, this issue is not sufficiently addressed on intra-community scales, as the characterization and comparison of short-term variations in urban environment is challenged by dynamic misalignment. Our study integrates discrete wavelet transforms (DWT) and dynamic time warping (DTW) to tackle this problem, and formulate practical pollution process indicators from a time-domain perspective. Specifically, original PM2.5 series are decomposed into multi-scale wavelet approximates, which are temporally realigned using DTW. Then, background variation series is estimated by seeking commonality among the majority of stations on time and frequency domains. Indicators are calculated by comparing local PM2.5 variation and estimated background variation, thus describing the local processes of background pollution episodes and discovering possible local pollution incidents. On this basis, empirical analysis in a campus network identified distinct information conveyed on different temporal scales. Case study discovered four phases within a 3-day period based on changes in dominating temporal scale of the background series, which is consistent with known pollution processes. Respective investigations of each phase show good capability in reflecting the influence of terrain, meteorology and dominant emission sources. Inter-station comparisons suggest significant influence from micro-scale spatial environment even when subject to exterior emissions. In general, the method can provide pertinent indicators for evaluation, prediction and optimization efforts in local pollution mitigation. The empirical results imply potentials of urban design measures for such mitigation, on which our future studies will focus.

Understanding the roles of rail stations: Insights from network approaches in the London metropolitan area

In strategic transport planning, rail stations play different roles at different scales, particularly in large cities and metropolitan regions, where movement structure shows polycentric patterns. However, there is a general lack of evidence-based studies investigating the relative network roles of rail stations. Therefore, this paper aims to explore rail stations' roles within and beyond metropolitan communities in London. Drawing on the Transport for London's newly released NUMBAT passenger origin-destination data, we unravel the travel structure and identify the stations' roles through network methods, community detection and Z-P scores. Our research has three results: First, London's passenger movement network can be subdivided into seven communities. The majority of stations within the same communities are geographically localised with few exceptions. Second, stations play distinct roles in handling the patterns of movement at different scales. Typically, there exists a set of stations that are significant for connecting between metropolitan communities, because the disruption of those stations will have a greater impact on overall network efficiency and affect a larger and more diverse group of passengers. Third, a classification of rail stations is developed based on stations' commitment at two scales. This classification allows planners to have a finer level of understanding of the network role of the stations and thus develop and adjust tailored (re)development strategies.

Are free-living nematodes effective environmental quality indicators? Insights from Bohai Bay, China

Free-living marine nematodes are sensitive biological indicators of environmental quality in marine sediments. This study investigated nematode communities in coastal waters in Bohai Bay, China. Nematode community taxonomic structure, colonizer–persister classes, maturity index (MI), index of trophic diversity (ITD), Shannon–Wiener diversity index (H′), together with the Hakanson’s Ecological Risk Index, were applied to assess the environmental quality of the investigated stations. The taxonomic structure, MI, ITD, and H′ varied among the study stations. In addition to the stochastic causes, variability in environmental variables together with metal concentrations explained, to a certain extent, the observed variations in nematode features. The differentiation in nematode species’ composition was attributed to changes in water depth, temperature, salinity, dissolved oxygen concentration, sediment medium diameter, organic matter, chlorophyll-a and pheophytin-a concentrations, and concentrations of manganese (Mn) and cobalt (Co). Mn was the most influential metal in explaining nematode diversity. MI appeared to be affected more by metals than by other environmental variables. According to the nematode features, the environmental quality ranged from bad to good at the majority of stations, although the rankings varied among different indices. Taxonomic structure, life strategy traits, ITD, and H′ were all effective in highlighting the areas subjected to severe metal contamination in the bay, and could provide more comprehensive assessments when used in combination. These indices suggested a more severe pollution status than the results obtained from the Hakanson’s Ecological Risk Index method, and may be more sensitive to ecological quality deterioration. However, their effectiveness requires further testing over larger spatial ranges and with more data.