Rainwater characteristics and interaction with atmospheric particle matter transportation analyzed by remote sensing around Beijing

Quantifying the direct and indirect impacts of urban waterlogging using input‒output analysis

Increasingly frequent urban waterlogging disasters are having profound social and economic consequences. An appropriate and integrated evaluation of the total economic impacts of such disasters is crucial for achieving effective urban disaster risk management and sustainable development. However, existing metrics are inadequate for measuring the economic impacts of rainfall events of different intensities and their ripple effects across regions. Moreover, their ecological impacts have received insufficient attention. To address these gaps, we developed an integrated assessment framework for analyzing urban waterlogging losses and evaluating their various impacts. Taking Beijing as a case study, we used the InfoWorks ICM model to simulate urban waterlogging disaster risks, quantified direct economic losses, and assessed their environmental impacts. Additionally, we estimated indirect economic losses using input-output analysis and explored spillover effects. The results revealed increasing trends of direct economic losses and environmental losses corresponding to a longer return period. We observed synergies between these losses and their spatial heterogeneity. However, indirect impacts far outweighed direct impacts, with the former being 2.43 times larger than the latter. The cascading effect resulting from damage to infrastructure was also particularly pronounced. The industrial and spatial heterogeneity of interregional impacts was striking, with eastern provinces evidencing the most significant effects. By mapping the transmission paths of disaster losses along industrial chains and across regions, this study provides inputs that could assist policymakers in developing more effective measures for preventing and mitigating urban waterlogging disasters.

Electro-galvanic alkalization and treatment of rainwater to obtain drinking water

Rainwater Electro-Galvanic Alkalization (EGA) was performed using copper and magnesium (1:1) electrode. Efficiently removal of pollutants without external energy consumption was carried out, in addition essential ions were dosed for alkalization of rainwater. The optimal system conditions were obtained using response surface methodology (RSM) by considering the following operating variables: flow rate and concentration of the supporting electrolyte (NaCl and CaCl2). Furthermore, the maximum efficiency of nitrate, ammoniacal nitrogen, colour, and turbidity removal was evaluated. The results showed that the response variables were mainly sensitive to the type of supporting electrolyte used and the flow rate. Under experimental conditions of 0.009 M (NaCl) and 20 mL min-1, the removal rate was 74.19%, 72.49%, and 81.43% for nitrates, colour, and turbidity, respectively, and the lowest concentration of ammoniacal nitrogen (0.99 mg L - 1 ) was obtained. The kinetic models for nitrate and colour were fitted to zero-order models with k = 0.33 mg L - 1 mi n - 1 and k = 0.933 Pt - Co , respectively. In addition, turbidity was fitted to a first-order model ( k = 0.1661 mi n - 1 ) , and ammoniacal nitrogen was fitted to a second-order model ( k = 0.0217 L m g - 1 mi n - 1 ) . The concentration increases of minerals such as Ca and Mg, which rises the rainwater alkalinity after treatment (pH shift from 6.1 to 8.91), was determined by inductively coupled plasma (ICP) analysis.

Response of air quality to short-duration high-strength human tourism activities at a natural scenic spot: a case study in Zhangjiajie, China

Short-duration high-strength human tourism activities (SHHTA) can result in more air pollution emissions owing to increase motor vehicle usage, energy consumption and cooking fume emissions. Because of the strong uncertainty of human tourism behaviour, it is difficult to accurately assess the impact of SHHTA on air quality of natural scenic spots. To overcome this difficulty, we propose a novel ensemble empirical mode decomposition and detrended cross-correlation analysis (EEMD-DCCA) model to assess the influence of short-duration high-strength human tourism activities (SHHTA) on air quality. Zhangjiajie in China was selected as the study area. Hourly concentrations of NO₂ were analysed from 1 January 2016 to 31 December 2018 at two monitoring sites, in an urban area and a scenic spot. Through EEMD, the main modes of NO₂ with short-duration high-frequency were obtained for both sites. The DCCA method was used to study the cross-correlation relationship between high-frequency modes of NO2 for the urban area and scenic spot. The results show that high-frequency modes of NO₂ between the two sites displayed long-range cross-correlation at the 24-h time scale. Furthermore, the quantitative impacts of meteorological factors (e.g. precipitation, temperature, and wind speed) on the DCCA exponent for high-frequency modes of NO₂ at the two sites were investigated. The novel model proposed in this study is not restricted by the uncertainty of pollution emission inventory. The relationship between meteorological factors and DCCA exponents corresponds to the hypothesis that NO₂ pollution of the natural scenic spot mainly came from SHHTA.

Bioaerosol is an important transmission route of antibiotic resistance genes in pig farms

Although pig farms are hotspots of antibiotic resistance due to intensive use of antibiotics, little is known about the abundance, diversity and transmission of airborne antibiotic resistance genes (ARGs). This study reports that bioaerosol is an important spread route of ARGs in pig farms. ARGs, mobile genetic elements (MGEs), and bacterial communities were investigated in both air and feces samples during winter and summer. The average concentration of airborne ARGs and MGEs during winter is higher than that during summer when using the ventilation system. The tetM is identified as the predominant airborne ARG with abundance of 6.3 ± 1.2 log copies/m³. Clostridium and Streptococcus are two dominant bacteria and several opportunistic pathogens are detected in air samples. High temperature is favorable for more diverse bacterial communities, but relative humidity has negative effects. The wind speed promotes the spread of airborne ARGs. The network analysis results show the average fecal contribution to airborne bacteria is 19.9% and 59.4% during summer and winter, respectively. Horizontal gene transfer plays an important role in the dissemination of airborne ARGs during winter (77.8% possibility), while a lower possibility of 12.0% in summer.

Quantify the Contribution of Dust and Anthropogenic Sources to Aerosols in North China by Lidar and Validated with CALIPSO

Persistent heavy haze episodes have repeatedly shrouded North China in recent years. Besides anthropogenic emissions, natural dust also contributes to the aerosols in this region. Through continuous observation by a dual-wavelength Raman lidar, the primary aerosol types and their contributions to air pollution in North China were determined. The following three aerosol types can be classified: natural dust, anthropogenic aerosols, and the mixture of anthropogenic aerosols and dust (polluted dust). The classification results are basically consistent with the classification results from the cloud–aerosol lidar and infrared pathfinder satellite observations (CALIPSO) satellite measurements. The relative bias of the lidar ratio between the Raman lidar and CALIPSO is less than 25% over 90% of the cases, indicating that the CALIPSO lidar ratio selection algorithm is reasonable. The classification results show that approximately 45% of aerosols below 1.8 km are contributed by polluted dust during our one year observations. The contribution of dust increased with height, from 6% at 500 m to 28% at 1,800 m, while the contribution of anthropogenic aerosols decreased from 49% to 25%. In addition, polluted dust is the major aerosol subtype below 1.0 km in spring (over 60%) and autumn (over 70%). Anthropogenic aerosols contribute more than 75% of air pollution in summer. In winter, anthropogenic aerosols prevailed (over 80%) in the lower layer, while polluted dust (around 60%) dominated the upper layer. Our results identified the primarily aerosol types to assess the contributions of anthropogenic and natural sources to air pollution in North China, and highlight that natural dust plays a crucial role in lower-layer air pollution in spring and autumn, while controlling anthropogenic aerosols will significantly improve air quality in winter.

Chemical characteristics of rainwater in the tropical rainforest region in northwestern Borneo

A comprehensive study of the chemical composition of rainwater was carried out from October 2016 to September 2017 in the equatorial tropical rainforest region of northwestern Borneo. Monthly cumulative rainwater samples were collected from different locations in the Limbang River Basin (LRB) and were later categorized into seasonal samples representing northeast monsoon (NEM), southwest monsoon (SWM), and inter-monsoon (IM) periods. Physical parameters (pH, EC, TDS, DO, and turbidity), major ions (HCO₃^-, Cl^-, Ca²⁺, Mg²⁺, Na⁺, and K⁺) and trace metals (Co, Ni, Cd, Fe, Mn, Pb, Zn, and Cu) were analyzed from collected rainwater samples. Rainwater is slightly alkaline with mean pH higher than 5.8. Chloride and bicarbonate are the most abundant ions, and the concentration of major ions in seasonal rainwater has shown slight variation which follows a descending order of HCO₃^-> Cl^-> Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ in NEM and Cl^- > HCO₃^- > Na⁺ > Ca²⁺ > K⁺ > Mg²⁺ in SWM and Cl^- > HCO₃^- > Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ in IM period. Trace metals such as Fe and Ni have shown dominance in seasonal rainwater samples, and all the metals have shown variation in concentration in different seasons. Variation in chemical characteristic of seasonal rainwater samples identified through piper diagram indicates dominance of Ca²⁺-Mg²⁺-HCO₃^- and mixed Ca²⁺-Mg²⁺-Cl^- facies during NEM, SWM, and IM periods. Statistical analysis of the results through two-way ANOVA and Pearson's correlation also indicates significant variation in physico-chemical characteristics. This suggests a variation in contributing sources during the monsoon seasons. Factor analysis confirmed the source variation by explaining the total variance of 79.80%, 90.72%, and 90.52% with three factor components in NEM, SWM, and IM rainwater samples with different loading of parameters. Enrichment factor analysis revealed a combined contribution of marine and crustal sources except K⁺ which was solely from crustal sources. Sample analysis of backward air mass trajectory supports all these findings by explaining seasonal variation in the source of pollutants reaching the study area. Overall, the results show that the chemical composition of seasonal rainwater samples in LRB was significantly influenced by natural as well as anthropogenic processes. These include (long-range and local) industrial activities, fossil fuel combustion, forest burning, transportation activities including road transport and shipping activities, and land-derived soil dust along with chemical constituents carried by seasonal wind.

Airborne bacterial communities and antibiotic resistance gene dynamics in PM2.5 during rainfall

The biotoxicity and public health effects of airborne bacteria and antibiotic resistance genes (ARGs) in fine particulate matter (PM_2.5) are being increasingly recognized. The characteristics of bacterial community composition and ARGs in PM_2.5 under different rainfall conditions were studied based on the on-site synchronous measurements in downtown Beijing. Marked differences were evident in the bacterial community characteristics of PM_2.5 before, during, and after rain events (p < 0.05). The rain intensities affected the bacterial community abundance in PM_2.5 and heavy rain had greater washing effects. The Proteobacteria (phylum level), α-Proteobacteria (class level), Pseudomonadales (order level), Pseudomonadaceae (family level), and Cyanobacteria (genus level) were the dominant bacterial taxa associated with PM_2.5 in Beijing during rain events. However, the bacteria at each level that displayed the biggest percentage variance was not the dominant type under different rain intensities. The ermB, tetW, and mphE genes were the primary ARGs, with abundances of 18 to 30 copies/m³, which was a relatively smaller value than other observations. Real-time monitoring of the meteorological condition of rain events and physicochemical properties of PM_2.5 were used to identify the main factors during rainfall. The bacterial community was sensitive to the ionic and metal element components of PM_2.5 during rainfall. The abundance of ARGs was closely correlated with some groups of the bacterial community, which were also close to the initial value before the rain. Statistical analysis demonstrated that temperature, relative humidity, and duration of rain were the primary meteorological factors for the biological characteristics. The ionic species, rather than metal elements, in PM_2.5 were the sensitive factors for the bacteria community and ARGs, which varied at the phylum, class, order, family, and genus levels. The observations provide insights for the biological risk assessment in an urban rainfall water and the potential health impact on citizens.

Change in the Distribution of Phosphorus Fractions in Aggregates under Different Land Uses: A Case in Sanjiang Plain, Northeast China

Phosphorus in agro-ecosystems has attracted much attention due to its impact on the nutrient supply of plants and the risk of loss of non-point source pollution. This study investigated the fraction distribution and release of phosphorus from soil aggregates structure under different land uses (rice, maize and soybean). The soil aggregates were characterized as large macro-aggregates (L-mac, >1 mm), small macro-aggregates (S-mac, 0.25⁻1 mm), micro-aggregates (MIC, 0.053⁻0.25 mm) and silt clay (SC, <0.053 mm) with the wet-sieving method. A sequential chemical extraction scheme was used to separate phosphorus into labile inorganic phosphorus (L-Pi), labile organic phosphorus (L-Po), moderately labile organic phosphorus (Ml-Po), iron-aluminum bound phosphorus (Fe.Al-P), calcium-magnesium bound phosphorus (Ca.Mg-P), humic phosphorus (Hu-P) and residual phosphorus (Re-P). Experimental results indicated that soil aggregates were mainly S-mac and MIC, followed by L-mac and SC, and they accounted for 52.16%, 25.20%, 14.23% and 8.49% in rice fields, 44.21%, 34.61%, 12.88% and 8.30% in maize fields, and 28.87%, 47.63%, 3.52% and 19.99% in soybean fields, respectively. Total nitrogen (TN), soil organic matter (SOM), Fe and Mn in soil aggregate fractions decreased with the reduction in soil aggregate grain-sizes. For phosphorus fractions (P-fractions), Fe.Al-P and Re-P tended to condense in L-mac and S-mac. MIC and SC were the primary carriers of Ca.Mg-P. Adsorption isotherm simulation results demonstrated that L-mac and S-mac have a strong capacity to retain phosphorus. In rice fields, phosphorus bioavailability and utilization rate were high. However, the P-fractions there were easily changed under aerobic-anaerobic conditions. Therefore, the risk of phosphorus loss during drainage should be given considerable attention.