North China City Research Articles

Investigate the important role of 3-D meteorological patterns in haze formation in the context of pollution reduction

Since 2013, heavy pollution episodes have occurred frequently over the North China Plain after unprecedented efforts to reduce primary pollutants. In this study, a pollution process in Tianjin, a typical city in North China, was selected to investigate the impact of 3-D meteorological patterns on PM2.5 and meteorological element profiles obtained by tethered balloons and meteorological towers. The pollution episode lasted 4 days with hourly PM2.5 concentrations exceeding 150 μg·m−3 for 81 h and a peak concentration of 377 μg·m−3. In the early stages of the first pollution period, wind speed with height showed an almost opposite trend to PM2.5 concentrations. In the vertical direction, weak winds were frequently accompanied by PM2.5 peaks, whereas strong winds were favourable for the diffusion of pollutants. In the later stage, a stable boundary layer with a height of approximately 600–700 m, thermal inversion layer capping the boundary layer, uniformly high-humidity atmosphere (>80 %), and relatively uniform distribution of wind speed across heights contributed to the high PM2.5, which remained within the boundary layer, and the continuous growth of surface PM2.5 concentrations. In the secondary pollution period, the successive regional transport of particles from Beijing and Baoding was the main reason for the two surface PM2.5 peaks in Tianjin. Different regional sources elevate PM2.5 levels, further extending the duration of haze pollution. The results reveal that 3-D meteorological conditions are the key reason for heavy pollution occurrence in the context of pollution reduction.

Combustion-driven inorganic nitrogen in PM2.5 from a city in central China has the potential to enhance the nitrogen load of North China

Inorganic nitrogen (NH4+ and NO3−) is a significant component of PM2.5, influencing not only regional ecological systems but also on other regions through the migration of air masses. However, few studies have simultaneously investigated the sources of NH4+ and NO3−, and their potential transport pathways remain poorly understood. Here, daily PM2.5 samples were collected in Jiaozuo, a key city in the air pollution transmission channel to the north China, from 1 September to 5 December, 2017. Major water-soluble inorganic ions and the isotope compositions of NH4+ and NO3− in PM2.5 were analyzed. The results indicated substantial amounts of inorganic nitrogen in PM2.5, particularly at high PM2.5 concentrations. The Bayesian isotope mixing model (MixSIAR) results revealed that combustion sources contributed 79.5 % to NO3− and 51.6 % to NH4+. Moreover, the medium to high potential source regions for combustion-related NH3 is basically consistent with combustion-related NOx. Therefore, stringent regulation of combustion emissions has the potential to mitigate inorganic nitrogen pollution in PM2.5 in Jiaozuo. The results of the forward trajectory cluster and PSCF (potential source contribution function) analysis revealed that a significant amount of combustion-driven inorganic nitrogen in PM2.5 from Jiaozuo will transport to downwind area, particularly north China. Combustion-driven inorganic nitrogen levels carried by these air masses exceeded half the average value for cities in North China during the same period. Our study highlights that combustion emissions dominate the inorganic nitrogen sources in PM2.5 and that substantial amounts of combustion-driven inorganic nitrogen can be transported from Jiaozuo to North China, potentially enhancing the nitrogen load in those areas.

Study on Spatial-Temporal Evolution, Decoupling Effect and Influencing Factors of Tourism Transportation Carbon Emissions: Taking North China as an Example

As global warming intensifies, reducing carbon emissions has become a global common mission. Tourism transportation is one of the important sources of carbon emissions, and reducing its carbon emissions is a key part of achieving China’s carbon reduction goals. Based on the panel data of various provinces and cities in North China from 2000 to 2022, this paper calculates the carbon emissions of tourism transportation by using the carbon emission coefficients of different transportation modes in different segments. Moreover, the temporal and spatial evolution of the tourism economy is systematically analyzed. The Tapio decoupling model and LMDI addition decomposition model are used to analyze the relationship between carbon emissions and tourism economic growth and the effects of 11 influencing factors on carbon emissions. The results show that: ① The carbon emission of tourism transportation in North China has experienced four stages: a steady growth period, a transitional adaptation period, a stable equilibrium period, and a drastic decline period. The overall carbon emission level of tourism transportation is as follows: Hebei Province > Shanxi Province > Inner Mongolia Autonomous Region > Beijing City > Tianjin City. ② The decoupling coefficient between tourism traffic carbon emissions and economic development fluctuates but mainly shows a weak decoupling state. ③ In terms of influencing factors, passenger size and passenger density have the greatest impact on the carbon emissions of tourism transportation.

Size distribution and source-specific risks of atmospheric elements in Dalian, a coastal city in north China

Trace elements in particles play an essential role in particle-related health and environmental effects and were recognized as primary source tracers. An understanding of the size-segregated elemental composition is critical for accurate source attribution and risk evaluation. The present study investigated the characteristics of 28 elements in the size-fractioned particles of a coastal megacity in north China. The size distribution of element concentration posed bimodal peak mode, and the proportion of most trace elements tended to increase with the decreasing particle size, especially toxic metals like Hg, Ga, Tl, As, Pb, Se, and Cd. Source attribution based on the size-segregated concentrations was conducted by positive matrix factorization. Natural sources such as crust and sea spray aerosol dominated the trace element concentration in the large particles, while anthropogenic sources such as traffic, biomass, coal combustion, and industry contributed the majority in the fine particles. The source-specific exposure risk calculation based on the size-segregated dataset showed a noncarcinogenic risk (HQ) of 6.62 for children, mainly contributed by Tl and As from biomass burning, petrochemical refining, and coal combustion. Compared with larger particles, the finer particles generated a higher HQ and carcinogenic risk, especially particles smaller than 0.49 μm. Fine particles primarily emitted from the petrochemical industry and coal combustion brought out the largest HQ, while fine particles from vehicle traffic and the petrochemical industry contributed the largest carcinogenic risk. Transport of sea spray aerosol contributed a considerable amount of the carcinogenic risk in the coastal urban area.

Built or Social environment? Effects of perceptions of neighborhood green spaces on resilience of residents to heat waves

Large cities in North China are at an increasing risk of heat waves. Several previous studies have emphasized the significance of heat wave resilience but they generally failed to sufficiently consider the social level and to explore the mechanisms involved. In the present study, we investigated heat wave resilience as a full-cycle process and elucidated the related factors at the level of environmental perception. We selected 60 neighborhoods that are vulnerable to high temperatures in the central urban area of Tianjin. Using a questionnaire (N = 734), we evaluated the residents' perceptions in terms of the built-environmental perception (BEP) and socio-environmental perception (SEP) of neighborhood green spaces, as well as assessing the resilience levels of residents in the three phases comprising heat wave preparation, resistance, and recovery. Structural equation models were constructed to identify the mechanisms that influenced resilience in each phase. The results demonstrated a significant and direct effect of SEP on heat wave preparation and on the resistance phase. In addition, both BEP and SEP were critical for the recovery of residents. Using multi-group analysis, we identified differences in the mechanisms that allowed residents to acquire heat wave resilience. Based on these findings, we suggest that green space planning and policies should focus on optimizing the built environment, but also on the roles of residents' social support networks because they are crucial for guaranteeing the sustainability of vulnerable groups and helping communities to cope with heat waves.

Characteristics of aerosol aminiums over a coastal city in North China: Insights from the divergent impacts of marine and terrestrial influences

Aminium ions, as crucial alkaline components within fine atmospheric particles, have a notable influence on new particle formation and haze occurrence. Their concentrations within coastal atmosphere depict considerable variation due to the interplay of distinctive marine and terrestrial sources, further complicated by dynamic meteorological conditions. This study conducted a comprehensive examination of aminiums ions concentrations, with a particular focus on methylaminium (MMAH+), dimethylaminium (DMAH+), trimethylaminium (TMAH+), and triethylaminium (TEAH+) within PM2.5, over varying seasons (summer, autumn, and winter of 2019 and summer of 2021), at an urban site in the coastal megacity of Qingdao, Northern China. The investigations revealed that the total concentration of particulate aminium ions (∑Aminium) was 21.6 ± 23.6 ng/m3, exhibiting higher values in the autumn and winter compared to the two summer periods. Considering diurnal variations during autumn and winter, concentrations of particulate aminium ions (excluding TEAH+) exhibited a slight increase during the day compared to night, with a notable peak during the morning hours. However, it was not the case for TEAH+, which was argued to be readily oxidized by ambient oxidants in the afternoon. Additionally, the ∑Aminium within the summer demonstrated markedly elevated levels during the day compared to night, potentially attributed to daytime sea fog associated with sea-land breeze interactions. Positive matrix factorization results indicate terrestrial anthropogenic emissions, including vehicle emission mixed with road dust and primary pollution, as the primary sources of MMAH+ and DMAH+. Conversely, TMAH+ was predominantly emitted from agricultural and marine sources. With the dominance of sea breeze in summer, TMAH+ was identified as a primary marine emission correlated with sea salt, while MMAH+, DMAH+, and TEAH+ were postulated to undergo secondary formation. Furthermore, a notable inverse correlation was observed between TMAH+ and methanesulfonate in PM2.5, consistent with dynamic emissions of sulfur-content and nitrogen-content gases reported in the literature.

Spatiotemporal variations in soil pollution by polycyclic aromatic hydrocarbons over a 20-year economic boom in different districts of a heavy industrial city in North China

Urbanization-related human activities, such as population aggregation, rapid industrial expansion, and intensified traffic, are key factors that impact local polycyclic aromatic hydrocarbon emissions and their associated health risks. Consequently, regions with varying degrees of urbanization within a megacity may exhibit diverse spatiotemporal patterns in the presence and distribution of soil polycyclic aromatic hydrocarbons, resulting in different levels of ecological risks for local inhabitants following the same period of development. In this study, we measured the concentrations of 16 polycyclic aromatic hydrocarbons in soil samples collected from industrial district and rural district in Tianjin (China) in 2018, and compared with polycyclic aromatic hydrocarbon data in 2001 from a previous study to characterize these regional variations in occurrence, source, and human risk of polycyclic aromatic hydrocarbons induced by urbanization with time and space. The results indicate the 20-year rapid urbanization and industrialization has differentially affected the composition, distribution and sources of polycyclic aromatic hydrocarbons in soils from different economic functional zones in Tianjin. Additionally, its impact on health risks in rural district appeared to be more significant than that in industrial district.

Study on multiscale-multivariate prediction and risk assessment of urban flood

Few studies have explored the impact of drivers on urban flood at multi-time scales, and few studies have assessed the urban flood risk based on accurate description and quantification of the intrinsic correlation between urban flood and its drivers. Therefore, this study develops a multiscale-multivariate prediction model for flood volume (FV) using wavelet transform, and a flood risk assessment model based on the joint distribution of FV and its key drivers using the Copula method. The downtown area of Zhengzhou City, a typical city in North China, is taken as the study area. The results reveal that land use change is the key driver for variation of urban rainstorm flood, and rapid urbanization led to the variation observed in 2005. The impact of land use change on FV primarily manifests at the long cycle scale, and the multiscale-multivariate prediction model demonstrates effective simulation (NSE = 0.957, R2 = 0.958, MAPE = 13.47%) and prediction capabilities (relative errors are all below 20%). Taking the FV exceeding the threshold corresponding to the frequency of 37.5% as an example, the maximum conditional risk probability under nine combinations of key drivers is 88.34%, while the minimum probability is only 6.83%, intuitively indicating the impact of rainstorm and urbanization on this risk. These findings can provide technical references for urban flood forecasting, urban water resources management and urban development planning.

Organic Carbon and Elemental Carbon in Two Dust Plumes at a Coastal City in North China

Organic Carbon and Elemental Carbon in Two Dust Plumes at a Coastal City in North China

Seasonal Particle Size Distribution and its Influencing Factors in a Typical Polluted City in North China

Seasonal Particle Size Distribution and its Influencing Factors in a Typical Polluted City in North China

Urban water resources management with energy constraint and carbon emission intensity under uncertainty: A dual objective optimization model

Water production, utilization and recycle in urban system associated with a large amount of energy consumption and carbon emission that has increasingly international attention under sustainable development goals. In this study, a comprehensive urban water resources system management model is proposed under water-energy-carbon nexus and multiple uncertainties. The water-energy-carbon nexus was reflected in the processes of water extraction, water production and sewage treatment, and two objectives of maximizing the system benefit and minimizing carbon emission is transformed into two linear programming problem through the dual-objective interval fractional programming. Considering the interaction among water supply/demand, pollutants discharge control, energy consumption and self-energy utilization, indirect carbon emission as the main constraints, the developed model is applied to a typical water and energy sources shortage area of Qingdao city in north China. Multiple scenarios related to energy consumption and carbon emission control are designed to search the relationship among energy consumption, carbon emission and water resources allocation schemes. The results indicated that water demand as the main driving factor of energy consumption and carbon emission in urban water system cannot be directly limited through water supply structure adjustment. Through introducing self-energy supply (sludge utilization) and energy consumption control, water resources allocation schemes and water supply structure would be changed to improve energy saving potential with an obvious decrease trend of carbon emission intensity from 0.7921 tCO2/MWh to 0.5544 tCO2/MWh. The model could effectively provide reasonable urban water system management strategies under different water allocation demands and carbon emission challenges scenarios and deal with multiple uncertainties in decision-making process.

Managing Extreme Rainfall and Flooding Events: A Case Study of the 20 July 2021 Zhengzhou Flood in China

On 20 July 2021, an extreme rainstorm battered Zhengzhou in China’s Henan Province, killing 302 people, including 14 individuals who drowned in a subway tunnel and 6 who drowned in a road tunnel. As the global climate warms, extreme weather events similar to the Zhengzhou flood will become more frequent, with increasingly catastrophic consequences for society. Taking a case study-based approach by focusing on the record-breaking Zhengzhou flood, this paper examines the governance capacity of inland cities in North China for managing extreme precipitation and flooding events from the perspective of the flood risk management process. Based on in-depth case analysis, our paper hypothesizes that inland cities in North China still have low risk perceptions of extreme weather events, which was manifested in insufficient pre-disaster preparation and prevention, poor risk communication, and slow emergency response. Accordingly, it is recommended that inland cities update their risk perceptions of extreme rainfall and flooding events, which are no longer low-probability, high-impact “black swans”, but turning into high-probability, high-impact “gray rhinos.” In particular, cities must make sufficient preparation for extreme weather events by revising contingency plans and strengthening their implementation, improving risk communication of meteorological warnings, and synchronizing emergency response with meteorological warnings.

A 1D-2D dynamic bidirectional coupling model for high-resolution simulation of urban water environments based on GPU acceleration techniques

Urban water pollution is an important issue of global concern. An integrated model that can be employed to efficiently and accurately simulate the whole system of urban surfaces, underground drainage systems and river and lake water environments is an important tool for comprehensively treating urban water pollution. Therefore, we developed a 1D-2D dynamic bidirectional coupling model for high-resolution simulation of the entire urban water environment (GAST-GCSE) comprising the graphics processing unit (GPU)-accelerated surface water flow and transport (GAST) model and the storm water management model (SWMM). The SWMM and GAST model were used for coupled simulation of hydrological and hydrodynamic quantities and water quality in 1D and 2D regions, respectively. The time steps of the two models are the same. Compute Unified Device Architecture (CUDA) parallel architecture programming and advanced model algorithms were adopted to improve the simulation accuracy and efficiency of the GAST model. The two models were dynamically coupled by calling the dynamic link library (DLL) in the SWMM and adding external functions. Two test cases were considered to verify the performance of the proposed model. The results showed that the proposed model provides high simulation accuracy. Compared with the results obtained by commercial software and theoretical values, the relative errors of the water quantity and water quality simulation results were less than 3%. The whole water environment of the main urban area of Changzhi city in North China was evaluated by the GAST-GCSE model, and the simulation results were compared with monitoring data and pure SWMM simulation results. The simulation errors at three waterlogging points were less than 14%, and the Nash efficiency coefficients (NSE) of the NH4+-N concentration in the three river sections were 0.77, 0.56 and 0.64, respectively. Notably, the model needed only 4.39 h to simulate rainfall production and confluence on a 2D surface, a river flood in complex terrain and its accompanying pollutant evolution process involving 3,849,428 cells over 4 h. This study provides new insights, ideas and tools for urban water environment simulation and change mechanism study.

Sources and solubility of aerosol phosphorus at a coastal site in northern China: Coarse versus fine particles and spring versus winter

Atmospheric P is of great importance as it is a major source of phosphorus (P) in open oceans, which can limit the primary productivity of ocean ecosystems and indirectly affect global climate. However, the understanding on the sources of atmospheric P, especially water-soluble P in different particle size and the factors that dominate P solubility is limited. This study characterizes P in fine and coarse particles during winter and spring in Qingdao, a coastal city in North China. The average concentration of total P in fine particles was 23 ± 15 ng/m3 in winter and 28 ± 24 ng/m3 in spring, while in coarse particles it was 21 ± 15 ng/m3 in winter and 15 ± 12 ng/m3 in spring. In spring, fine particles had notably higher concentrations of total P than coarse particles, while in winter, the difference between the two particle sizes was not pronounced. Regardless of the concentration of total P, the sources of total P varied distinctly between fine and coarse particles in both seasons. The concentration of soluble P is obviously higher in spring than in winter for the fine particles but coarse particles show no significant difference between the two seasons. P solubility is always higher in spring compared to winter regardless of the particle size, which can be due to higher relative humidity. Both soluble P concentrations and P solubility were significantly higher in fine particles compared to coarse particles during winter and spring. A higher contribution of combustion emission and higher acidity can be the reasons for the higher P solubility in fine particles in winter. The findings suggest that a comprehensive evaluation of the ecological impacts of atmospheric phosphorus should consider its sources, aerosol acidity, humidity, and differences among fine and coarse particles.

Evaluation of carbon emission efficiency based on urbanscaling law: take 308 cities in China as an example.

The development of urban low carbonization is an important foundation for achieving the goal of "dual carbon." The differences in spatial differences based on scientific methods revealing the characteristics of urban carbon emissions and its efficiency are of great significance for shaping the green low-carbon land space pattern. This article uses urban standard models and scale adjustments to the urban index (SAMIs) identification in 2006-2017 308 Chinese urban carbon discharge standards and efficiency time and space evolution pattern and their characteristics. The results of the study show that there is a stable secondary relationship between carbon emissions and urban population scale, which means that the growth rate of urban carbon emissions is significantly lagging behind the growth of urban systems. The analysis of urban carbon emission efficiency based on SAMI values shows significant spatial heterogeneity: the carbon emission efficiency of cities in North China, East China, and Northeast China is lower than that of other regions. In terms of temporal changes, there was a phenomenon of SAMI high-value cities moving north and low-value cities moving south between 2006 and 2017, and △SAMIs showed a pattern of hot in the west and cold in the east. This study broadens the applicability of urban scaling law models in urban environmental research, providing a new perspective for evaluating urban carbon emission efficiency and China's energy conservation and emission reduction work.

The synergy between temporal and spatial effects of human activities on CO2 emissions in Chinese cities

Based on the IPAT (I = human impact, P = population, A = affluence, and T = technology) framework, we used the temporal and spatial Logarithmic Mean Divisia Index decomposition approach to investigate whether the temporal and spatial impacts of human activities on carbon emissions are synergistic in 330 prefecture-level cities in China from 2003 to 2017. The results showed that carbon emissions in Chinese cities increased to different degrees during the study period, and a spatial distribution pattern could be recognized, with high levels in the East and South, and low levels in the West and North. Carbon emissions in cities in North China and in the eastern coastal areas displayed a cluster pattern higher than the national average level, although in most cities were lower than the national average. With respect to temporal effects, we found that although the decrease in carbon intensity played an important role in reducing carbon emissions in cities, it was not sufficient to offset the incremental gains associated with economic growth. The effect of population change was not apparent. With respect to spatial effects, we found that the effect of carbon intensity in most cities enhanced the difference with the national average carbon emission, whereas the effect of economic development reduced it. The effect of population size on spatial differences in carbon emission was bound by the Heihe–Tengchong line, forming a distribution pattern in which the difference in emissions was enhanced in the Southeast and reduced in the Northwest. The temporal and spatial impacts of human activities on carbon emissions in most cities were not synergistic. These findings can provide a reference for formulating differentiated carbon emission reduction policies among cities.

A new hybrid PM[Formula: see text] volatility forecasting model based on EMD and machine learning algorithms.

In recent years, the frequent occurrence of air pollution incidents has seriously affected people's health and life. Therefore, PM[Formula: see text], as the main pollutant, is an important research object of air pollution at present. Effectively improving the prediction accuracy of PM[Formula: see text] volatility makes the PM[Formula: see text] prediction content perfect, which is an important aspect of PM[Formula: see text] concentration research. The volatility series has an inherent complex function law, which drives the volatility movement. When machine learning algorithms such as LSTM (Long Short-Term Memory Network) and SVM (Support Vector Machine) are used for volatility analysis, a high-order nonlinear form is used to fit the functional law of the volatility series, but the time-frequency information of the volatility has not been utilized. Based on EMD (Empirical Mode Decomposition) technique, GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model and machine learning algorithms, a new hybrid PM[Formula: see text] volatility prediction model is proposed in this study. This model realizes time-frequency characteristic extraction of volatility series through EMD technology, and integrates residual and historical volatility information through GARCH model. The simulation results of the proposed model are verified by comparing the samples of 54 cities in North China with the benchmark models. The experimental results in Beijing showed that MAE (mean absolute deviation) of hybrid-LSTM decreased from 0.00875 to 0.00718 compared with LSTM, and hybrid-SVM based on the basic model SVM also significantly improved generalization ability, and its IA (index of agreement) improved from 0.846707 to 0.96595, showing the best performance. The experimental results show that the hybrid model is superior to other considered models in terms of prediction accuracy and stability, which verifies that the hybrid system modeling method is suitable for PM[Formula: see text] volatility analysis.

Smart charging strategy for electric vehicles based on marginal carbon emission factors and time-of-use price

As the penetration of electric vehicles (EVs) has grown rapidly in recent years, the stability and safety of electric grids are inevitably affected. Uncontrolled EV charging can reduce electric grid efficiency and increase greenhouse gas emissions (GHG). This occurs when EV charging aligns with peak demand, requiring the activation of fossil fuel-powered plants to meet the electricity demand. The time-of-use (TOU) price is a traditional management strategy for smoothing the demand power curve, but it does not consider carbon emissions. This study proposes a smart charging strategy based on an improved local search genetic algorithm (LS-GA) that considers both the TOU price and marginal emission factors (MEF). Over 47,000 charging events from 45 charging stations in Tianjin, a major city in North China, were used to simulate the charging pattern and demand. Four different charging strategies are considered in the study. The results indicate that the proposed smart charging strategy can reduce the cost and carbon emissions by up to 27% and 16% compared with uncontrolled charging, respectively. This study demonstrates the significant potential of using optimization methods in the EV smart charging problem.

Ascertaining priority control pollution sources and target pollutants in toxic metal risk management of a medium-sized industrial city

Re-suspended surface dust (RSD) often poses higher environmental risks due to its specific physical characteristics. To ascertain the priority pollution sources and pollutants for the risk control of toxic metals (TMs) in RSD of medium-sized industrial cities, this study took Baotou City, a representative medium-sized industrial city in North China, as an example to systematically study TMs pollution in RSD. The levels of Cr (242.6 mg kg−1), Pb (65.7 mg kg−1), Co (54.0 mg kg−1), Ba (1032.4 mg kg−1), Cu (31.8 mg kg−1), Zn (81.7 mg kg−1), and Mn (593.8 mg kg−1) in Baotou RSD exceeded their soil background values. Co and Cr exhibited significant enrichment in 94.0 % and 49.4 % of samples, respectively. The comprehensive pollution of TMs in Baotou RSD was very high, mainly caused by Co and Cr. The main sources of TMs in the study area were industrial emissions, construction, and traffic activities, accounting for 32.5, 25.9, and 41.6 % of the total TMs respectively. The overall ecological risk in the study area was low, but 21.5 % of samples exhibited moderate or higher risk. The carcinogenic risks of TMs in the RSD to local residents and their non-carcinogenic risks to children cannot be ignored. Industrial and construction sources were priority pollution sources for eco-health risks, with Cr and Co being the target TMs. The south, north and west of the study area were the priority control areas for TMs pollution. The probabilistic risk assessment method combining of Monte Carlo simulation and source analysis can effectively identify the priority pollution sources and pollutants. These findings provide scientific basis for TMs pollution control in Baotou and constitute a reference for environmental management and protection of residents' health in other similar medium-sized industrial cities.

Research into the Spatiotemporal Characteristics and Influencing Factors of Technological Innovation in China’s Natural Gas Industry from the Perspective of Energy Transition

Promoting technological innovation in the natural gas industry is a feasible means of achieving energy transition. Guided by the geographic innovation theory, this article carries out research on the scale, technical fields, capabilities, and influencing factors of technological innovation in the natural gas industry of 312 Chinese prefecture-level cities, making use of the cusp catastrophe model, the center of gravity and standard deviational ellipse, exploratory spatial data analysis, and geographically and temporally weighted regression (GTWR). The research shows the following: (1) Technological innovation in China’s natural gas industry has continuously expanded in terms of scale, with the number of participating cities increasing, showing a spatially uneven pattern of local agglomeration and national diffusion. (2) There have been significant innovation achievements in natural gas equipment and engineering, but natural gas utilization is lagging in comparison, with drilling, new materials, environmental protection, pipe network engineering, and digital services becoming frontier fields, and collaborative innovation with the thermoelectric, metalworking, automotive, and other related industries having been initially established. (3) The unevenness of technological innovation capabilities is obvious, with the core advantages of Beijing–Tianjin being continuously strengthened and Sichuan–Chongqing, the Yangtze River Delta, the Pearl River Delta, Shandong Peninsula, and Liaodong Peninsula forming high-level technological innovation capability agglomerations. (4) The spatiotemporal pattern of technological innovation capability is the result of multiple factors, with northeastern cities mainly being affected by natural gas demands, northwestern cities being highly sensitive to capital strength, eastern cities mostly relying on urban development, and cities in North China mainly being bolstered by the strength of talent. (5) It is necessary to carry out further multi-agent and multi-scale future research on technological innovation in the natural gas industry and its relationship with the energy transition and to explore the interactivity of the influencing factors. This study may provide strategies for technological innovation in the natural gas industry from the perspective of the energy transition.