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Natural gas is the primary fuel used in U.S. residences, especially during winter, when cold temperatures drive the heating demand. In this study, we use daily county-level gas consumption data to assess the spatial patterns of the relationships and sensitivities of gas consumption by U.S. households considering outdoor temperatures. Linear-plus-plateau functions are found to be the best fit for gas consumption and are applied to derive two key coefficients for each county: the heating temperature threshold (Tcrit) below which residential heating starts and the rate of increase in gas consumption when the outdoor temperature drops by one degree (Slope). We then use interpretable machine learning models to evaluate the key building properties and socioeconomic factors related to the spatial patterns of Tcrit and Slope based on a large database of individual household properties and population census data. We find that building age, employment rates, and household size are the main predictors of Tcrit, whereas the share of gas as a heating fuel and household income are the main predictors of Slope. The latter result suggests inequalities across the U.S. with respect to gas consumption, with wealthy people living in well-insulated houses associated with low Tcrit and Slope values. Finally, we estimate potential reductions in gas use in U.S. residences due to improvements in household insulation or a hypothetical behavioral change toward reduced consumption by adopting a 1°C lower Tcrit than the current value and a reduced slope. These two scenarios would result in 25% lower gas consumption at the national scale, avoiding 1.24 million MtCO2 of emissions per year. Most of these reductions occur in the Midwest and East Coast regions. The results from this study provide new quantitative information for targeting efforts to reduce household gas use and related CO2 emissions in the U.S.

With the urgent need to implement the EU countries pledges and to monitor the effectiveness of Green Deal plan to reduce greenhouse gases emissions, Monitoring Reporting and Verification tools are needed to track how emissions are changing for all the sectors. Current official inventories only provide annual estimates of national CO2 emissions with a lag of 1+ year which do not capture the variations of emissions due to recent shocks including COVID lockdowns and economic rebounds, supply chains tensions and rising prices of energy and commodities, war in Ukraine. Here we present a near-real-time dataset of daily fossil fuel and cement emissions to monitor country-level emissions from January 2019 through December 2021 for 27 European Union countries and the United Kingdom. This dataset is called Carbon Monitor Europe. The data are calculated separately for six sectors: power, industry (incl. cement production), ground transportation, domestic aviation, international aviation, residential emissions which includes the built environment. Daily CO2 emissions are estimated from a large set of activity data compiled from different sources, including hourly to daily electrical power generation data, monthly production data and production indices of industry, daily mobility data and indices for the ground transportation. Individual flight location data and monthly data were for aviation sector estimates. Monthly fuel consumption data downscaled in time with daily air temperature are used to estimate daily emissions from commercial and residential buildings. The goal of this dataset is to improve the timeliness and temporal resolution of emissions for European countries, to inform the public and decision makers about current emissions changes in Europe.

Cities generate the majority of CO2 emissions, the largest climate change contributor. Near real-time CO2 emission monitoring and modeling at relatively high resolutions are beneficial to fill the knowledge gaps for the spatial and temporal emission patterns in different regions, and to provide the public and the policymakers with accurate and timely information on major emission sources and emission amounts for better public awareness and decision making. On the other hand, there are even larger knowledge and information shortage in Global South counties for the city-/community- level CO2 emissions due to the lack of well-monitored official data with high latency and low transparency.Data-driven models with big data embedded can be one of the most robust and efficient approaches for addressing those challenges in regions without well-documented data. Therefore, we developed the Building Integral Gridded Carbon Emission Disaggregating Model (BIGCarbonEDM) that disaggregates Scope 1&2 CO2 emissions to community-level (with a resolution at 500 meters) using machine learning models trained with building- to regional-level features. Multiple open datasets were used as model inputs, 1) building-level datasets: Microsoft Building Footprints, OpenStreetMap, and OpenStreetMap Building, 2) regional-level datasets: Global Human Settlement, World Settlement Footprint, VIIRS Nightlights, Local Climate Zone, Copernicus Digital Elevation, and land surface temperature, 3) economical and census datasets were collected from the national statistical report and world bank surveys, and 4）city-level near real-time CO2 emission: Carbon Monitor City (https://cities.carbonmonitor.org/), one of our previous projects.BIGCarbonEDM for the first time proposed the approach for capturing the emission patterns for the community level based on building-level and regional-level features and provides the near real-time CO2 emission for twelve major cities in Egypt, South Africa, and Turkey. The cities are Adana, Trabzon, Ordu, and Manisa for Turkey; Cairo, Alexandria, Luxor, and Sheikh Zayed for Egypt; and Johannesburg, Tshwane, Ekurhuleni, and eThekwini for South Africa. BIGCarbonEDM was designed as a modular platform including modules for data collection, data fusion, spatial and temporal emission feature learning, emission estimation model, and data visualization. BIGCarbonEDM modules can be updated and modified separately, which simplifies the improvements and extensions of the final delivered dataset, also all the individual modules can be used by the research community for other relevant research.

Russia is the largest natural gas supplier to the European Union (EU). The invasion of Ukraine was followed by a cutoff of gas supplies from Russia to many EU countries, and the EU is planning to ban or drastically reduce its dependence on Russia. We provide a dataset of daily gas consumption in five sectors (household and public building heating, power, industry, and other sectors) with supply source shares in the EU27 (27 EU member countries) and UK from 2016 to 2022. The datasets are available at Zenodo platform: https://doi.org/10.5281/zenodo.7549233 (Zhou et al., 2022). The dataset separates the contributions of Russian imports, liquefied natural gas (LNG) imports, and other supply sources to both direct supply and storage supply for gas consumption estimations. The dataset was developed with a gas network flow simulation model based on mass flow balance by combining data from multiple datasets including daily ENTSOG (European Network of Transmission System Operators for Gas) pipeline gas transport and storage, ENTSOE (European Network of Transmission System Operators for Electricity) daily power production from gas, and Eurostat monthly gas consumption statistics per sector. The annual consumption data were validated against the BP Statistical Review of World Energy and Eurostat datasets. We secondly analyzed the share of gas supplied by Russia in each country to quantify the “gap” that would result from a cessation of all Russian exports to Europe. Thirdly, we collected multiple data sources to assess how national gaps could be alleviated by (1) reducing the demand for heating in a plausible way using the lower envelope of gas empirical consumption – temperature functions, (2) increasing power generation from sources other than gas, (3) transferring gas savings from countries with surplus to those with deficits, and (4) increasing imports from other countries like Norway, the USA, Australia, and northern African countries from either pipelines or LNG imports, accounting for existing capacities. Our results indicate that it should be theoretically possible for the EU to collectively make up for a sudden shortfall of Russian gas by combining the four solutions together, provided a perfect collaboration between EU countries and the UK to redistribute gas from countries with surplus to those with deficits. Further analyses are required to investigate the implications with respect to the costs, including social, economic, and institutional dimensions; political barriers; and negative impacts on climate policies, with inevitable increases in CO2 emissions if the use of coal is ramped up in the power sector.

Chuanlong Zhou,1 Jie Bao,2 Hantong Hu,1 Siting Ye,3 Xiaomei Shao,4 Yi Liang,4 Jianqiao Fang4 1Department of Acupuncture, the Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China; 2School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China; 3Department of Tuina, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China; 4Key Laboratory of Acupuncture and Neurology of Zhejiang Province, the Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, People’s Republic of ChinaCorrespondence: Chuanlong Zhou, Department of Acupuncture, the Third Affiliated Hospital of Zhejiang Chinese Medical University, No. 219 Moganshan Road, XiHu District, Hangzhou, 310005, People’s Republic of China, Email zhouchuanlong6255@163.com

A recently developed open source tool kit named the Trajectory based Potential Source Apportionment (TraPSA) was used to identify the sources of respirable particulates at Kochi, India. Using 24-h average particulate matter data from samples collected at five regulatory monitoring stations over the five-year period from January 2011 to October 2016, local and regional scale analyses were made. Concentration field analysis was performed using back trajectories generated by Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model with inputs from atmospheric reanalysis data. Conditional bivariate probability function analyses were made using local meteorology data to identify the influence of local sources. Most of the stations indicated the contribution from local traffic activities during low wind conditions and from a nearby industrial area especially during high speed winds. Back trajectory analysis identified potential source areas in Kerala as well as in nearby state of Tamil Nadu as contributing to the air quality at Kochi. Arabian sea on the western side was also observed to be a potential source area for Kochi. The study demonstrated the utility of TrapSA as a tool for deriving information about the potential source areas affecting particulate matter mass concentrations.

Gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), and particle-bound mercury (PBM) were measured continuously in Rochester, NY (NY43) from January 2012 to December 2014. Continuous measurements of ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen oxide (NO), nitrogen dioxide (NO2), particulate matter (PM2.5), and meteorological data were also made at this site. A principal component analysis (PCA) of the resulting 15 variables showed that the ambient mercury in Rochester was primarily produced by non-local sources in contrast to earlier studies that showed that local sources were present. Positive matrix factorization (PMF) analysis of the atmospheric mercury and other pollutant species concentrations showed that transport and atmospheric processes have become the major source of mercury in Rochester. Conditional bivariate probability function (CBPF) and potential source contribution function (PSCF) were used to identify local and distant mercury sources. The results in this study showed that the closure of a coal-fired power plant and promulgation of several fuel quality policies reduced local mercury emissions making long-distance transport the major source of mercury in Rochester.

Background: Headache attacks severely impaired life quality and increase economic burden of migraineurs. Electroacupuncture (EA) has been used world-widely to treat several pain-related diseases including migraine. However, whether EA with low- or high-frequency exerts distinct analgesic effect remains unknown and need to further study.Methods/Design: This study is a randomized, prospective randomized trial with three parallel arms. A total of 129 migraine outpatients will be randomly allocated to 2Hz EA group, 100Hz EA group and control group. Twelve treatment sessions will be performed over a four-week period. The primary outcome will be measured by frequency of migraine attacks at baseline, four-week of treatment and consecutive four-week follow-up. Secondary outcome measures will be number of days with migraine, frequency of taking ibuprofen, Visual Analogue Scale (VAS), Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS) and Migraine Specific Quality of Life questionnaire (MSQ), safety and compliance evaluation.Discussion: This trial is expected to comparative effect of electroacupuncture with different frequency on headache attacks in migraineur.Trial registration: Chinese Clinical Trial Registry, ChiCTR-1800017259.Registered on 20 July 2018.Registered on 20 July 2018.

Hexabromocyclododecane (HBCDD) is a hazardous, persistent, bioaccumlative brominated flame retardant. To investigate how its use has affected the Great Lakes, total HBCDD (∑HBCDD) concentrations and temporal trends in homogenized whole fish samples from the Great Lakes region (1978 to 2016) were determined. ∑HBCDD concentrations (ng/g ww) for each lake are Erie (0.49-2.60), Ontario (3.12-8.90), Michigan (3.91-9.01), Superior (5.69-13.1), and Huron (5.57-13.7). Early years (1978 to 1992) showed no significant trend. However, recent trends (2004 to 2016) suggest concentrations are increasing in Lakes Erie and Ontario, decreasing in Lakes Superior and Michigan, and not changing in Lake Huron. Decreasing trends for Lakes Superior and Michigan are likely the result of decreased usage of the compound globally, regionally, and locally. For the other lakes, increasing or zero trends are consistent with food web changes due to invasive species and climate change, which has caused more intense storms and less ice cover leading to increased sediment resuspension.

Measurements of ambient speciated mercury (Hg) concentrations and Hg wet deposition were made at two urban sites (Bronx, NY and Rochester, NY) and one rural site (Potsdam, NY) in New York State in 2013 and 2014 to: 1) determine the factors influencing Hg wet deposition concentrations, 2) identify the contribution of gaseous oxidized Hg (GOM) and particulate bound Hg (PBM) scavenging to Hg wet deposition concentrations, and 3) identify potential source areas associated with high concentration events. The Bronx had the highest mean gaseous elemental Hg (GEM) and GOM concentrations, Rochester had the highest mean PBM and the lowest GOM concentrations, and Potsdam had the lowest mean GEM and PBM concentrations. The annual volume weighted mean (VWM) Hg concentrations and Hg wet deposition fluxes in the Bronx, Rochester, and Potsdam were significantly different with mean values of 10.3 ± 8.16, 10.2 ± 9.06, and 5.07 ± 1.79 ngL−1 and 8.45 ± 0.64, 6.65 ± 0.21, and 5.25 ± 0.49 μg/m2 year−1, respectively. Hg wet deposition flux and precipitation depth were positively correlated at all three sites as were Hg concentration in precipitation and weekly GOM concentrations at the Bronx and Potsdam sites. Scavenging coefficients (SC) of 680, 630, 850 for GOM and 410, 320, and 410 for PBM at Bronx, Rochester, and Potsdam, respectively, suggest GOM is responsible for most of the scavenged Hg. Measured GOM and PBM concentrations were relatively constant before precipitation events and Hg concentrations in precipitation did not vary significantly during precipitation events implying the scavenging process mainly occurred in clouds. VWM Hg concentrations, monthly accumulated Hg flux, and SCs for GOM and PBM were higher at the urban sites and significantly different for non-snow and snow events. Local sources appeared more important at the rural site while regional sources affected high urban concentrations.

Long-term atmospheric mercury measurements at Underhill, VT (VT99), and Huntington Forest, NY (NY20), from 1992 to 2014 and 2005 to 2014, respectively, were used to determine concentration trends using Mann–Kendall’s tau test with Sen’s slope estimator. These data, measured generally downwind of large Hg sources in the Midwestern United States, provide the longest record of ambient Hg concentrations available in the United States. At VT99, concentrations of gaseous element mercury (GEM), gaseous oxidized mercury (GOM), and particle-bound mercury (PBM) declined at rates of −1.8, −3.2, and −6.7%/year, respectively. At NY20, GEM and GOM concentrations declined at rates of −1.6 and −7.8%/year, respectively; however, PBM concentrations increased at a rate of 2.0%/year, which is likely related to winter wood burning. A trajectory ensemble analysis using the potential source contribution function indicates the source locations associated with high mercury concentrations changed from Toronto–Buffalo and Pennsylva...

Polybrominated diphenyl ethers (PBDEs) were widely used as fire retardants and have been detected throughout the Great Lakes (GL) ecosystem. The concentration trends (after fish age normalization) of PBDEs in top predator fish (lake trout and walleye) of the GLs were determined from 1979 to 2016, which includes most of the period when PBDEs were manufactured and used in this region. The fish samples were collected by two national (U.S. and Canada) long-term monitoring and surveillance programs. Trends in total concentrations (age-normalized) of the five major PBDE congeners (BDE-47, 99, 100, 153, and 154) found in fish across all five lakes have varied over time. Significant increases were observed from 1990 to 2000 (16.3% per year). Rapidly decreasing concentrations (-19.5% per year) were found from 2000 to 2007. Since 2007, the decreasing trend has become smaller (less than -5.5% per year) and relatively unchanged from 2011 to 2015. BDE-47, the congener with the highest concentrations in lake trout, has decreased continuously (ranging from -6.7% to -16.2% per year) in all lakes except Lake Erie. This decrease can be associated with the voluntary and regulatory phase out of production and/or usage of PBDEs since 2000. However, it has been offset by recent (since 2007) increasing trends of the other four higher brominated BDE congeners, especially BDE-100 and 154. Production and usage of commercial penta- and octa- BDE mixtures containing primarily the five major PBDE congeners was discontinued in 2004 in the U.S.A. and 2008 in Canada. These results indicate increasing fish uptake and bioaccumulation of higher brominated BDE congeners may be related to the transformation of BDE-209 to lower brominated BDE compounds in the GL environment or food web. Considering the abundance of BDE-209 in existing products and sediment in GL region, the duration of the unchanging total PBDE concentration trend in GL fish could be longer than expected.

Following the phase-out of polybrominated diphenyl ether (PBDE) and hexabromocyclododecane (HBCDD) flame retardants (FRs) from North American markets, the use of alternative FRs has increased. In this study the occurrence and spatiotemporal distributions of 18 dechlorane analogues (collectively referred to as DECs) and 20 alternative brominated FRs (referred to as ABFRs, i.e., brominated FRs other than PBDEs and HBCDD) were investigated in top predator fish megacomposites (i.e., a composite of all 50 fish) collected yearly from each of the Great Lakes from 2004 to 2016. Frequently detected substances include dechlorane 602, 603, 604 Component B, anti- and syn-dechlorane plus, and chlordene plus, as well as several brominated benzene FRs (i.e., hexabromobenzene, pentabromotoluene, and tetrabromo- o-chlorotoluene). Concentrations of ΣDECs and ΣABFRs ranged from 0.33-31.9 ng/g lipid weight (lw) (0.01-8.3 ng/g wet weight or ww) and 0.91-54.7 ng/g lw (0.09-7.1 ng/g ww), respectively. Flame retardant contamination exhibited chemical-specific spatial variations across the five lakes. Concentrations of ΣABFRs in Lake Erie fish were generally lower than those from other lakes. By contrast, fish ΣDEC concentrations were highest in Lake Ontario and the composition of dechlorane analogues differed significantly between Lake Ontario and the other lakes, indicating likely point-source influences. Temporal analyses revealed declining trends of ΣDECs and ΣABFRs in most lakes except Lake Erie, with age corrected trend slopes of -13.5% to -8.8% and -20.1% to -7.0% per year, respectively.

The water quality prediction performance of machine learning models may be not only dependent on the models, but also dependent on the parameters in data set chosen for training the learning models. Moreover, the key water parameters should also be identified by the learning models, in order to further reduce prediction costs and improve prediction efficiency. Here we endeavored for the first time to compare the water quality prediction performance of 10 learning models (7 traditional and 3 ensemble models) using big data (33,612 observations) from the major rivers and lakes in China from 2012 to 2018, based on the precision, recall, F1-score, weighted F1-score, and explore the potential key water parameters for future model prediction. Our results showed that the bigger data could improve the performance of learning models in prediction of water quality. Compared to other 7 models, decision tree (DT), random forest (RF) and deep cascade forest (DCF) trained by data sets of pH, DO, CODMn, and NH3-N had significantly better performance in prediction of all 6 Levels of water quality recommended by Chinese government. Moreover, two key water parameter sets (DO, CODMn, and NH3-N; CODMn, and NH3-N) were identified and validated by DT, RF and DCF to be high specificities for perdition water quality. Therefore, DT, RF and DCF with selected key water parameters could be prioritized for future water quality monitoring and providing timely water quality warning.

Concentration patterns and temporal trends of legacy persistent, bioaccumulative and toxic (PBT) contaminants were determined using the Great Lake Fish Monitoring and Surveillance Program (GLFMSP) top predator fish data from 1999 to 2014 and applying Kendall-Theil robust regression after cluster-based age normalization. For most Great Lakes sites, significant decreasing concentration trends ranging from -4.1% to -21.6% per year (with the only exception being mirex in Lake Erie walleye) were found for PBTs including polychlorinated biphenyls (PCBs), dichloro-diphenyl-trichlorethane (DDTs), dieldrin, endrin, chlordane, oxychlordane, nonachlor, mirex, and hexachlorobenzene (HCB) reflecting the successful historical and ongoing reduction of fugitive releases and remediation efforts in the U.S. and Canada including physical removal (dredging) coupled with sediment sequestration. Generally, lower concentrations and faster decreasing trends are observed in western/northern sampling sites compared to eastern/southern sites as the former sites are generally more remote from population centers and industrial activities. PCBs, which can be released from ongoing sources, have the highest concentration, the second slowest decreasing trend, and increasing mass fractions of the contaminants studied suggesting that they will continue to be the legacy contaminant of greatest concern into the future.

Polyhalogenated carbazoles (PHCZs) were recently discovered in Great Lakes sediment and other aquatic systems. However, knowledge about their bioaccumulation and potential risks to fish and wildlife remains very limited. The present study investigated PHCZs in Great Lakes lake trout ( Salvelinus namaycush) and walleye ( Sander vitreus; Lake Erie only) composites collected between 2004 and 2016. Median concentrations of ∑PHCZs by lake ranged from 54.7 to 154 ng/g lipid weight or lw (6.8-28.0 ng/g wet weight). Dominant congeners included 3,6-dichlorocarbazole, 1,3,6-tribromocarbazole, and 1,3,6,8-tetrachlorocarbazole. The highest ∑PHCZs concentrations were found in Lakes Michigan and Ontario fish, followed by Lake Huron, whereas Lakes Erie and Superior fish contained the lowest concentrations. Congener profiles of PHCZs also exhibited spatial variations. After age normalization to minimize fish age influence on bioaccumulation rates, fish ∑PHCZs' concentrations declined significantly over time in all lakes except Lake Erie, with slopes ranging from -10.24% to -3.85% per year. The median toxic equivalent (TEQ) of PHCZs due to their dioxin-like activity was determined to range from 8.7 to 25.7 pg/g lw in Great Lakes fish. This study provides the first insight into the bioaccumulation and spatiotemporal trends of PHCZs in Great Lakes and suggests the need for further research on this group of chemicals.

Light-absorbing, atmospheric particles have gained greater attention in recent years because of their direct and indirect impacts on regional and global climate. Atmospheric black carbon (BC) aerosol is a leading climate warming agent, yet uncertainties in the global direct aerosol radiative forcing remain large. Based on a year of aerosol absorption measurements at seven wavelengths, BC concentrations were investigated in Dhanbad, the coal capital of India. Coal is routinely burned for cooking and residential heat as well as in small industries. The mean daily concentrations of ultraviolet-absorbing black carbon measured at 370nm (UVBC) and black carbon measured at 880nm (BC) were 9.8±5.7 and 6.5±3.8μgm-3, respectively. The difference between UVBC and BC, Delta-C, is an indicator of biomass or residential coal burning and averaged 3.29±4.61μgm-3. An alternative approach uses the Ǻngstrom Exponent (AE) to estimate the biomass/coal and traffic BC concentrations. Biomass/coal burning contributed ~87% and high temperature, fossil-fuel combustion contributed ~13% to the annual average BC concentration. The post-monsoon seasonal mean UVBC values were 10.9μgm-3 and BC of 7.2μgm-3. Potential source contribution function analysis showed that in the post-monsoon season, air masses came from the central and northwestern Indo-Gangetic Plains where there is extensive agricultural burning. The mean winter UVBC and BC concentrations were 15.0 and 10.1μgm-3, respectively. These higher values were largely produced by local sources under poor dispersion conditions. The direct radiative forcing (DRF) due to UVBC and BC at the surface (SUR) and the top of the atmosphere (TOA) were calculated. The mean atmospheric heating rates due to UVBC and BC were estimated to be 1.40°Kday-1 and 1.18°Kday-1, respectively. This high heating rate may affect the monsoon circulation in this region.

Mercury (Hg) concentration trends in top predator fish (lake trout and walleye) of the Great Lakes (GL) from 2004 to 2015 were determined by Kendall-Theil robust regression with a cluster-based age normalization method to control for the effect of changes in lake trophic status. When data from the GLs (except Lake Erie) are combined, a significant decreasing trend in the lake trout Hg concentrations was found between 2004 and 2015 with an annual decrease of 4.1% per year, consistent with the decline in regional atmospheric Hg emissions and water Hg concentrations. However, a breakpoint was detected with a significant decreasing slope (-8.1% per year) before the breakpoint (2010), and no trend after the breakpoint. When the lakes are examined individually, Lakes Superior and Huron, which are dominated by atmospheric Hg inputs and are more likely than the lower lakes to respond to declining emissions from areas surrounding the GL, have significant decreasing trends with rates between 5.2 and 7.8% per year from 2004 to 2015. These declining trends appear to be driven by decreasing regional atmospheric Hg emissions although they may be partly counterbalanced by other factors, including increasing local emissions, food web changes, eutrophication, and responses to global climate change. Lakes Michigan, Erie and Ontario may have been more impacted by these other factors and their trends changed from decreasing to non-decreasing or increasing in recent years.

The structures of nanoscale zero-valent iron (nZVI) particles evolving during reactions and the reactions are influenced by the evolved structures. To understand the removal process in detail, it is important to investigate the relationships between the reactions and structural evolution. Using high resolution-transmission electron microscopy (HR-TEM), typical evolved structures (sheet coprecipitation and cavity corrosion) of nZVI in anoxic Co2+ solutions were revealed. The system pH (pH measured in mixture), which controls the stability of coprecipitation and the nZVI corrosion rate, were found to be the determining factors of structural evolutions. X-ray photoelectron spectroscopy (XPS) results indicated that the formation and dissolution of sheet structure impacts on the ratio of Fe(0) on the nZVI surface and the surface Co2+ reduction. The cavity structure provides the possibility of Co migration from the surface to the bulk of nZVI, leading to continuous removal. Subacidity conditions could accelerate the evolution and improve the removal; the results of structurally controlled reactions further indicated that the removal was suspended by the sheet structure and enhanced by cavity structure. The results and discussion in this paper revealed the “structural influence” crucial for the full and dynamical understanding of nZVI reactions.