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[Display omitted] • PDDA (5–40 g/kg TSS) reduced hydrogen production from sludge by up to 37 %. • The dark fermentation process was hindered by decreased enzyme activity. • Hydrogen production was more strongly inhibited than hydrogen consumption. • The acetate pathway was converted to the propionate and butyrate pathways. • The dominant bacteria were crowded out by other enriched microorganisms. Polydimethyldiallylammonium chloride (PDDA) is an excellent flocculant for wastewater purification and sludge dewatering, but whether it poses a threat to hydrogen production from waste activated sludge is not known. In this study, the effect and underlying mechanism of PDDA on the dark fermentation of sludge was investigated. The results showed that PDDA reduced cumulative hydrogen production from 3.8±0.1 to 2.4±0.1 mL/g volatile suspended solids at 40 g/kg total suspended solids. PDDA impeded the dark fermentation process by inhibiting the activity of key enzymes, presenting a stronger inhibitory effect on the hydrogen production process than the hydrogen consumption process. Additionally, PDDA inhibited Firmicutes by enriching other microorganisms, thereby impeding hydrogen production via the acetate pathway. This study deepens the understanding of the potential effects of PDDA on sludge treatment and provides a theoretical basis for alleviating the negative effects of quaternary ammonium-based cationic flocculants. [ABSTRACT FROM AUTHOR]

Quantifying the impact of urbanization on extreme climate events is significant for ecosystem responses, flood control, and urban planners. This study aimed to examine the urbanization effects on a suite of 36 extreme temperature and precipitation indices for the Beijing-Tianjin-Hebei (BTH) region by classifying the climate observations into three different urbanization levels. A total of 176 meteorological stations were used to identify large cities, small and medium-size cities and rural environments by applying K-means cluster analysis combined with spatial land use, nighttime light remote sensing, socio-economic data and Google Earth. The change trends of the extreme events during 1980–2015 were detected by using Mann-Kendall (MK) statistical test and Sen's slope estimator. Urbanization effects on those extreme events were calculated as well. Results indicated that the cool indices generally showed decreasing trends over the time period 1980–2015, while the warm indices tended to increase. Larger and more significant changes occurred with indices related to the daily minimum temperature. The different change rates of temperature extremes in urban, suburban and rural environments were mainly about the cool and warm night indices. Urbanization in medium-size cities tended to have a negative effect on cool indices, while the urbanization in large cities had a positive effect on warm indices. The significant difference of urbanization effect between large and medium-size cities lay in the daily maximum temperature. Results also demonstrated the scale effect of the urbanization on the extreme temperature events. However, the results showed little evidence of the urban effect on extreme precipitation events in the BTH region. This paper explored the changes in temperature and precipitation extremes and qualified the urbanization effects on those extreme events in the BTH region. The findings of this research can provide new insights into the future urban agglomeration development projects. Unlabelled Image • Urbanization effects on extreme climate events were estimated. • The scale effects of urbanization on extreme events were revealed. • Significant changes occurred with indices related to the daily minimum temperature. • Main difference between urban and suburban areas lay in daily maximum temperature. • Little evidence of the urbanization effect on precipitation extremes was found. [ABSTRACT FROM AUTHOR]

[Display omitted] • Anaerobic digestion of sludge was substantially inhibited by PDDA. • PDDA at 38.1 g/kg TS reduced cumulative methane production by 64.3 ± 0.2%. • Quaternary amine groups hindered the release of organic matter in the sludge. • Enzyme activity was downregulated by PDDA-induced oxidative stress. • PDDA reduced the relative abundance of acetotrophic methanogens. The effects and key mechanisms of polydimethyldiallylammonium chloride on anaerobic digestion of waste activated sludge were investigated. Polydimethyldiallylammonium chloride at 38.1 g/kg total solids substantially reduced cumulative methane production from 138.2 ± 5.5 to 49.4 ± 5.0 L CH 4 /kg volatile solids added, a reduction of 64.3 ± 0.2%. The quaternary ammonium groups on polydimethyldiallylammonium chloride agglomerated sludge flocs by neutralizing negatively charged amino groups in in extracellular polymeric substances, which hindered the release of organic matter. Quaternary ammonium groups induce oxidative stress by inducing the production of reactive oxygen species, thereby inhibiting the activity of anaerobic digestive enzymes. In addition, quaternary amine groups reduced the abundance of hydrolyzing bacteria, acidifying bacteria, and acetylotrophic methanogens. Oxidative stress could be an underappreciated mechanism that quaternary ammonium groups deteriorate anaerobic digestion, which could be transformative for understanding the potential risks of quaternary ammonium cationic flocculants in biological sludge treatment. [ABSTRACT FROM AUTHOR]

Perfluorooctanoic acid (PFOA), as a recalcitrant organic pollutant, inevitably enters wastewater treatment facilities and is enriched in settled sludge. However, the potential impact of PFOA on sludge treatment has never been documented. In this study, the effect of PFOA on anaerobic digestion of sewage sludge and its underlying mechanism were investigated through batch and long-term experiments. The presence of PFOA was found to be deleterious for methane production from sewage sludge. 170 mg/kg total solids PFOA reduced the cumulative methane production from 197.1 ± 1.92–159.9 ± 3.10 mL/g volatile solids. PFOA induces the production of reactive oxygen species, which directly leads to cell inactivation and interferes with methane production. PFOA stimulates microorganisms to secrete more extracellular polymeric substances (mainly proteins), which not only hinders the solubilization of organic matter but also down-regulate enzyme activities to inhibit acidification and methanogenesis. In addition, PFOA reduces the diversity of microorganisms, especially the abundance of acid-producing bacteria and methanogens, making the microbial community unfavorable for methane production. [Display omitted] • PFOA significantly inhibits methane production in sludge anaerobic digestion. • PFOA triggers oxidative stress in anaerobic digestion of sewage sludge. • PFOA stimulates the secretion of extracellular polymeric substances (mainly proteins). • PFOA down-regulates enzyme activities to inhibit acidification and methanogenesis. • PFOA reduces the abundance of acid-producing bacteria and methanogens. [ABSTRACT FROM AUTHOR]

Obtaining high-quality precipitation datasets with a fine spatial resolution is of great importance for a variety of hydrological, meteorological and environmental applications. Satellite-based remote sensing can measure precipitation in large areas but suffers from inherent bias and relatively coarse resolutions. Based on the high accuracy surface modeling method (HASM), this study proposed a new downscaling method, the high accuracy surface modeling-based downscaling method (HASMD), to derive high-quality monthly precipitation estimates at a spatial resolution of 0.01° by downscaling the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation estimates in China. A scale transformation equation was introduced in HASMD, and the initial value was set by including the explanatory variables related to precipitation. The performance of HASMD was evaluated by comparing the results yielded by HASM and the combined method of HASM, Kriging, IDW and the geographical weighted regression (GWR) method (GWR-HASM, GWR-Kriging, GWR-IDW). Analysis results indicated that HASMD performed better than the other four methods. High agreement was achieved for HASMD, with bias values ranging from 0.07 to 0.29, root mean square error (RMSE) values ranging from 9.53 mm to 47.03 mm, and R2 values ranging from 0.75 to 0.96. Compared with the original IMERG precipitation products, the downscaling accuracy with HASMD improved up to 47%, 47%, and 14% according to bias, RMSE and R2, respectively. HASMD was able to capture the spatial variation in monthly precipitation in a vast region, and it might be potentially applicable for enhancing the spatial resolution and accuracy of remotely sensed precipitation data and facilitating their application at large scales. [ABSTRACT FROM AUTHOR]

In the context of global warming, significant differences in temperature changes occur in different regions. Obtaining a more accurate temperature distribution is important for studying regional climate change. A high accuracy surface modeling (HASM) was introduced using the air temperature output from the weather research and forecasting (WRF) model as the driving field, and observation data from meteorological stations as the accuracy control conditions to obtain 30 years of high accuracy temperature fields in the Heihe River Basin. Verified by ground‐based observations, the WRF model has a limited ability to simulate temperature conditions and performs worse in low altitude areas and in winter. The fusion results of the HASM decrease the mean absolute error from 2.21 to 0.9°C and decrease the root mean square error from 2.58 to 1.16°C, thus significantly increasing the accuracy compared with the original simulation from the WRF model and resulting in a more reasonable temperature analysis. The Mann–Kendall test shows clear warming in the Heihe River Basin, but there is a mutation year of 1998 in winter and annual average. The space–time distributions of and variations in the temperature from the HASM results are also analysed by dividing the study period into three subperiods. The analysis shows consistent warming except for the winter 2001–2010 period compared with the winter 1991–2000 period, in which the northeast area of the lower reaches of the Heihe River Basin was cool. Temperature changes in the Heihe River Basin also show that the sparsely populated Gobi Desert in the north and the southern high mountains show extreme sensitivity to climate change. [ABSTRACT FROM AUTHOR]

The miniaturization of transistors led to advances in computers mainly to speed up their computation. Such miniaturization has approached its fundamental limits. However, many practices require better computational resources than the capabilities of existing computers. Fortunately, the development of quantum computing brings light to solve this problem. We briefly review the history of quantum computing and highlight some of its advanced achievements. Based on current studies, the Quantum Computing Advantage (QCA) seems indisputable. The challenge is how to actualize the practical quantum advantage (PQA). It is clear that machine learning can help with this task. The method used for high accuracy surface modelling (HASM) incorporates reinforced machine learning. It can be transformed into a large sparse linear system and combined with the Harrow-Hassidim-Lloyd (HHL) quantum algorithm to support quantum machine learning. HASM has been successfully used with classical computers to conduct spatial interpolation, upscaling, downscaling, data fusion and model-data assimilation of eco-environmental surfaces. Furthermore, a training experiment on a supercomputer indicates that our HASM-HHL quantum computing approach has a similar accuracy to classical HASM and can realize exponential acceleration over the classical algorithms. A universal platform for hybrid classical-quantum computing would be an obvious next step along with further work to improve the approach because of the many known limitations of the HHL algorithm. In addition, HASM quantum machine learning might be improved by: (1) considerably reducing the number of gates required for operating HASM-HHL; (2) evaluating cost and benchmark problems of quantum machine learning; (3) comparing the performance of the quantum and classical algorithms to clarify their advantages and disadvantages in terms of accuracy and computational speed; and (4) the algorithms would be added to a cloud platform to support applications and gather active feedback from users of the algorithms. [ABSTRACT FROM AUTHOR]

Temperature changes have a major impact on all aspects of human society and have attracted global attention. The scarcity of observation data and the inaccuracy of the models make obtaining accurate temperature distributions a challenge. This study introduces high-accuracy surface modeling (HASM) combined with temperature simulations from the Weather Research and Forecasting (WRF) model and temperature records from observation stations to investigate the spatiotemporal characteristics of temperature in the Beijing-Tianjin-Hebei region during the period of 1956–2005. Leave-one-out cross-validation is applied to verify the temperature fields before and after the fusion of the models. The results indicate that the WRF model has a limited ability to simulate temperature conditions, but the overall deviation across the region is relatively large. The fusion results of the HASM decrease the mean absolute error (MAE) and the root-mean-square error (RMSE) by half in most instances, and the correlation between the fusion data and observation data is approximately 0.01–0.03 higher than that with the WRF simulation data. Based on the fusion data, obvious warming trends are observed during 1976–2005. In general, temperatures in spring, summer, and autumn increase rapidly from 1996 to 2005 but from 1976 to 1995 in winter. The substantial fluctuations in the interannual temperature during 1996–2005 in summer, autumn, and winter may be related to the frequent occurrence of extreme weather. The spatial distribution of temperature change differs in each season during 1956–1995. A dramatic increase in temperature occurs in the western part of the study area during 1996–2005 but with no seasonal difference. [ABSTRACT FROM AUTHOR]

Chang'e-5 (CE-5), China's first sample-return mission, has successfully landed in Oceanus Procellarum near Mons Rümker. It is important to have a detailed study of the geological evolution of the CE-5 sample return region. This work aims to study the geological background, topography, geomorphology, major chemical composition, mineralogy, and chronology of the landing site region. First, we used the map of topography obtained by the Kaguya TC merged Digital Terrain Model (DTM) to analyze the topographic characteristics. Then, we used the Kaguya Multiband Imager (MI) reflectance data to derive FeO and TiO2 abundance and the hyperspectral data of the Moon Mineralogy Mapper (M3) onboard the Chandrayaan-1 spacecraft to study the mineralogy of the landing site region. Later, we defined and dated the geological units of the landing area using the crater size–frequency distribution (CSFD) method. Finally, we conducted a detailed analysis of the volcanism and tectonism that occurred in the CE-5 landing area. The study region has experienced multi-stage magmatic activities (~3.36 Ga to ~1.22 Ga) and formed multiple mare units with different chemical and mineral compositions. The relationship between the wrinkle ridges cut by small impact craters suggests that the U7/Em5 has experienced Copernican aged tectonism recently ~320 Ma. The U7/Em5 unit where the Chang'e-5 sample return mission landed is dominantly composed of mature pyroxene and the basalts are mainly high-iron and mid-titanium basalts. Additionally, the analysis of pure basalt in the U7/Em5 suggests that the samples returned by the CE-5 mission may contain the ejecta and ray materials of young craters, including sharp B, Harding, Copernicus, and Aristarchus. [ABSTRACT FROM AUTHOR]

• The first global projection of future population exposure to heatwave was conducted. • Modified framework of exposure projection considering urban expansion was developed. • Coastal and low-altitude cities in both rich and poor countries are most at risk. • Climate contributes most to exposure, followed by its interaction with urbanization. • The impact of future urban expansion on heatwave exposure was highlighted. The increasing exposure to extreme heatwaves in urban areas from both climate change and the urban heat island (UHI) effect poses multiple threats and challenges to human society. Despite a growing number of studies focusing on extreme exposure, research advances are still limited in some aspects such as oversimplification of human exposure to heatwaves and neglect of perceived temperature as well as actual body comfort, resulting in unreliable and unrealistic estimates of future results. In addition, little research has performed comprehensive and fine-resolution global analyses in future scenarios. In this study, we present the first global fine-resolution projection of future changing urban population exposure to heatwaves by 2100 under four shared socioeconomic pathways (SSPs) considering urban expansion at global, regional, and national scales. Overall, global urban population exposure to heatwaves is rising under the four SSPs. Temperate and tropical zones predictably have the greatest exposure among all climate zones. Coastal cities are projected to have the greatest exposure, followed closely by cities at low altitudes. Middle-income countries have the lowest exposure and the lowest inequality of exposure among countries. Individual climate effects contributed the most (approximately 46.4%) to future changes in exposure, followed by the interactive effect between climate and urbanization (approximately 18.5%). Our results indicate that more attention needs to be paid to policy improvements and sustainable development planning of global coastal cities and some low-altitude cities, especially in low- and high-income countries. Meanwhile, this study also highlights the impact of continued future urban expansion on population exposure to heatwaves. [ABSTRACT FROM AUTHOR]

Satellite observation is one of the main methods used to monitor the global distribution and variation of atmospheric carbon dioxide (CO2). Several CO2 monitoring satellites have been successfully launched, including Japan's Greenhouse Gases Observing SATellite (GOSAT), the USA's Orbiting Carbon Observatory-2 (OCO-2), and China's Carbon Dioxide Observation Satellite Mission (TanSat). Satellite observation targeting the ground-based Fourier transform spectrometer (FTS) station is the most effective technique for validating satellite CO2 measurement precision. In this study, the coincident observations from TanSat and ground-based FTS were performed numerous times in Beijing under a clear sky. The column-averaged dry-air mole fraction of carbon dioxide (XCO2) obtained from TanSat was retrieved by the Department for Eco-Environmental Informatics (DEEI) of China's State Key Laboratory of Resources and Environmental Information System based on a full physical model. The comparison and validation of the TanSat target mode observations revealed that the average of the XCO2 bias between TanSat retrievals and ground-based FTS measurements was 2.62 ppm, with a standard deviation (SD) of the mean difference of 1.41 ppm, which met the accuracy standard of 1% required by the mission tasks. With bias correction, the mean absolute error (MAE) improved to 1.11 ppm and the SD of the mean difference fell to 1.35 ppm. We compared simultaneous observations from GOSAT and OCO-2 Level 2 (L2) bias-corrected products within a ±1° latitude and longitude box centered at the ground-based FTS station in Beijing. The results indicated that measurements from GOSAT and OCO-2 were 1.8 ppm and 1.76 ppm higher than the FTS measurements on 20 June 2018, on which the daily observation bias of the TanSat XOC2 results was 1.87 ppm. These validation efforts have proven that TanSat can measure XCO2 effectively. In addition, the DEEI-retrieved XCO2 results agreed well with measurements from GOSAT, OCO-2, and the Beijing ground-based FTS. [ABSTRACT FROM AUTHOR]