Weather Index Research Articles

Role of Climate-Smart Agriculture towards Sustainable Food Production

Agriculture output is currently under severe threat from global climate change. The two biggest challenges of this century are thought to be reducing greenhouse gas (GHG) emissions and maintaining food security. The idea of Climate-Smart Agriculture (CSA) can address the difficulties facing agricultural development. By improving adaptation, lowering GHG emissions, and assuring domestic food security, it can achieve this in a sustainable manner. There hasn't been any comprehensive research done yet on CSA advancements in developed and developing nations. Reviewing the recent developments, difficulties, and potential future orientations of CSA will be both timely and beneficial. The definition of CSA and its development objectives are discussed in this article. The most recent CSA developments in industrialized and developing nations are then examined. The current issues and difficulties in CSA are examined and identified. Finally, suggestions for the future prospects and directions of CSA are made. The primary directions for CSA's future development include using cutting-edge internet technology to ensure the security of agricultural information, enhancing crop management and planting practices, providing "internet + weather" services to raise the caliber of agricultural services, and offering insurance based on agricultural weather indices. This review article offers fresh concepts and methods for enhancing ecological environmental defense, encouraging environmentally friendly agricultural development, and reducing the impact of climate change.

Extending MESMER-X: a spatially resolved Earth system model emulator for fire weather and soil moisture

Abstract. Climate emulators are models calibrated on Earth system models (ESMs) to replicate their behavior. Thanks to their low computational cost, these tools are becoming increasingly important to accelerate the exploration of emission scenarios and the coupling of climate information to other models. However, the emulation of regional climate extremes and water cycle variables has remained challenging. The MESMER emulator was recently expanded to represent regional temperature extremes in the new “MESMER-X” version, which is targeted at impact-related variables, including extremes. This paper presents a further expansion of MESMER-X to represent indices related to fire weather and soil moisture. Given a trajectory of global mean temperature, the extended emulator generates spatially resolved realizations for the seasonal average of the Canadian Fire Weather Index (FWI), the number of days with extreme fire weather, the annual average of the soil moisture, and the annual minimum of the monthly average soil moisture. For each ESM, the emulations mimic the statistical distributions and the spatial patterns of these indicators. For each of the four variables considered, we evaluate the performances of the emulations by calculating how much their quantiles deviate from those of the ESMs. Given how it performs over a large range of annual indicators, we argue that this framework can be expanded to further variables. Overall, the now expanded MESMER-X emulator can emulate several climate variables, including climate extremes and soil moisture availability, and is a useful tool for the exploration of regional climate changes and their impacts.

Projections of severe weather and the impacts on transmission line towers in Santa Catarina, Brazil, under future scenarios of global climate change

Transmission line towers are highly exposed to weather hazards. In southern Brazil, several damages to transmission lines have been reported due to severe weather events. Climate change projections indicate that the frequency and magnitude of extreme events may increase, making planning and adaptation of the transmission system exposure even more critical. In this work, we propose a severe weather index (SWI) as a proxy for detecting severe storms that can potentially result in extreme weather events. The SWI combines thresholds of instability indices based on observed severe events around the transmission tower-line systems. These indices were calculated using the downscaling by the Eta model at 20 km resolution of three global climate model projections and at 5 km of one global climate model for the historical period and the near future. The results show that the SWI captures the severe storms observed in the region. All downscaling projections agree with the increase of extreme weather events in the near future and the expansion of vulnerable areas. The constructed index can be employed to assess the risks and to plan the power transmission system better.

Assessing Peer Influence on Farmers’ Climate Adaptations: An Experimental Adoption of Index Insurance and Savings in Uzbekistan

AbstractStrengthening climate resilience requires farmers to select climate adaptation strategies like weather index insurance. Acknowledging that decision-making is not isolated, this study explores simultaneous peer imitation in climate adaptation choices consisting of index insurance, savings, and their interaction. We present results from a lab-in-the-field experiment that introduces innovative index insurance. Findings indicate significant and strong imitation attitudes. While the bigger peer surrounding seems relevant in the static perspective, the closer surrounding gains importance in the dynamic perspective. Additionally, credit, trust, and practical understanding stimulate adoption. Community-based extension interventions and credit-bundled products may increase index insurance diffusion and improve climate resilience.

Ambiguity attitudes and demand for weather index insurance with and without a credit bundle: experimental evidence from Kenya

We investigate the impact of ambiguity attitudes on the willingness-to-pay (WTP) for index insurance among female smallholders in Kenya. We gauge incentive-compatible measures of ambiguity aversion and insensitivity in the domain of gains and losses, as well as loss aversion. Next, we setup a framed experiment to measure WTP for insurance with basis risk. For a random subsample we introduce an alternative ‘rebate’ insurance, comparable to an insurance purchased through a loan – repaid in good years and deducted from payout in bad ones – that is expectedly more palatable for the loss averse. We find that ambiguity aversion significantly increases WTP for the standalone insurance, while loss aversion reduces it as expected. The former result is seemingly at odds with previous evidence from the field, but is consistent with a setting in which insurance ambiguity engenders relatively less disutility compared to the vagaries of weather. We show that this apparent divergence is not caused by differences in the method used to estimate ambiguity aversion compared to existing field studies. Rather, we exploit exogenous variation in the familiarity with insurance within our sample to show that it is explained away by the role of experience with the novel technology—a previously underestimated mediator. Ambiguity aversion hinders adoption at early stages but increases when the insurance is better understood. The rebate scenario, instead, all but cancels the effect of loss aversion on WTP, but the increased contractual ambiguity results in significantly lower bids by the ambiguity averse. In the lab, the WTP for rebate-type insurance-credit bundles is not different from that of the actuarially equivalent standalone insurance, implying that evidence from the field on greater uptake for the former may be attributable to liquidity constraints and time discounting effects, rather than to behavioural traits.

Late Oligocene monsoonal climate in the Lunpola Basin, central Tibetan Plateau: evidence from palaeosol records

The Asian monsoon affects the natural patterns of climate in China and its origin and evolution have been a matter of much debate in palaeoclimatology. Results from recent studies indicate that the Asian monsoon had reached the subtropical zone by at least c. 41 Ma and expanded to the central Tibetan Plateau during the Late Oligocene, although more geological evidence is required to confirm its spatial and temporal evolution. The well-developed Late Oligocene palaeosol in the Lunpola Basin, central Tibetan Plateau is an ideal material for addressing the issue. Observations of various climatic indicators suggest that these palaeosols were forest cinnamon soils, as shown by the compound Bt and Bk horizons, abundant clay coatings and carbonate nodules, and the diagnostic chemical composition of clays in the Bt horizon. A high chemical weather index, the Rb/Sr ratios and high contents of illite/smectite mixed-layer minerals show that these palaeosols experienced intense weathering and leaching during pedogenesis. The mean annual temperature and mean annual precipitation during the developmental period of the palaeosols were 10.4–14.8°C and 615–1128 mm, respectively, as estimated by empirical formulas, which are comparable with those of cinnamon soils in current monsoon climates. This study provides important independent evidence from palaeosols for studies of the evolution of the Asian monsoon. Supplementary material: The data about the clay chemical composition and climatic indicators of palaeosol and modern soils are available at https://doi.org/10.6084/m9.figshare.c.6850703 Thematic collection: This article is part of the Mesozoic and Cenozoic tectonics, landscape and climate change collection available at: https://www.lyellcollection.org/topic/collections/mesozoic-and-cenozoic-tectonics-landscape-and-climate-change

A Multi-Input Convolutional Neural Networks Model for Earthquake Precursor Detection Based on Ionospheric Total Electron Content

Earthquakes occur all around the world, causing varying degrees of damage and destruction. Earthquakes are by their very nature a sudden phenomenon and predicting them with a precise time range is difficult. Some phenomena may be indicators of physical conditions favorable for large earthquakes (e.g., the ionospheric Total Electron Content (TEC)). The TEC is an important parameter used to detect pre-earthquake changes by measuring ionospheric disturbances and space weather indices, such as the global geomagnetic index (Kp), the storm duration distribution (Dst), the sunspot number (R), the geomagnetic storm index (Ap-index), the solar wind speed (Vsw), and the solar activity index (F10.7), have also been used to detect pre-earthquake ionospheric changes. In this study, the feasibility of the 6th-day earthquake prediction by the deep neural network technique using the previous five consecutive days is investigated. For this purpose, a two-staged approach is developed. In the first stage, various preprocessing steps, namely TEC signal improvement and time-frequency representation-based TEC image construction, are performed. In the second stage, a multi-input convolutional neural network (CNN) model is designed and trained in an end-to-end fashion. This multi-input CNN model has a total of six inputs, and five of the inputs are designed as 2D and the sixth is a 1D vector. The 2D inputs to the multi-input CNN model are TEC images and the vector input is concatenated space weather indices. The network branches with the 2D inputs contain convolution, batch normalization, and Rectified Linear Unit (ReLU) activation layers, and the branch with the 1D input contains a ReLU activation layer. The ReLU activation outputs of all the branches are flattened and then concatenated. And the classification is performed via fully connected, softmax, and classification layers, respectively. In the experimental work, earthquakes with a magnitude of Mw5.0 and above that occurred in Turkey between 2012 and 2019 are used as the dataset. The TEC data were recorded by the Turkey National Permanent GNSS Network-Active (TNPGN-Active) Global Navigation Satellite System (GNSS) stations. The TEC data five days before the earthquake were marked as “precursor days” and the TEC data five days after the earthquake were marked as “normal days”. In total, 75% of the dataset is used to train the proposed method and 25% of the dataset is used for testing. The classification accuracy, sensitivity, specificity, and F1-score values are obtained for performance evaluations. The results are promising, and an 89.31% classification accuracy is obtained.

The effect of weather index insurance on social capital: Evidence from rural Ethiopia

AbstractWe study the effect of weather index insurance (WII) uptake on social capital. We measure individual social capital using experimental and survey‐based measures and relate it to the actual purchase of WII. We use propensity score matching (PSM) and an instrumental variable (IV) to address endogeneity concerns. Our descriptive and PSM estimates show that WII uptake negatively and significantly affects social capital. We find that insured households contribute less to the public good than uninsured households. We also find qualitatively and quantitatively similar results using our IV approach, yet insufficient power renders most of our IV estimates insignificant. We however report robust significant effects of instrumented WII uptake on sociopsychological outcomes—WII uptake increases perceptions of self‐sufficiency and free‐riding behavior: these are potential channels through which a negative effect on social capital comes about. Although far from conclusive, our paper provides several pieces of evidence that suggest WII uptake negatively affects social capital.

Prediksi Luas Area Terbakar Menggunakan Fire Weather Index dan Frekuensi Titik Panas di Jambi

Forest fires occur every year in Indonesia, one of the regions with the highest forest fires is Jambi Province. Significant losses and negative impacts due to forest fires cause the need for an effort to prevent forest fires early on with the detection of forest and land fires. One method that can provide information about the level of forest fire based on daily weather data input is the Fire Weather Index (Fire Weather Index / FWI) system, which was first developed by Canada. This study aims to estimate burn area in the Jambi region by using temperature, rainfall, humidity, and wind speed data. Other supporting data are hotspot frequency data from NASA-FIRMS satellites and data fraction of burn area from GFED satellites on a daily scale of the period 2006-2016. In this study an analysis of the relationship between these data variables and burn area estimation was carried out using multiple linear regression methods then validated to see the level of suitability of the output model forecasts. The results showed that the predictor variables that had the highest relationship were hotspots frequency, Buildup Index (BUI), and FWI index with correlation values of 0.888, 0.739 and 0.753, respectively. The estimation model of the resulting burnt area is: Burned Area = -966.6146918 + (7.519631195 × BUI) + (147.4865469 × FWI) + (14.5373858 × Hotspots) + 116, with an RMSE value of 635.524 and MAE of 491.38

Drivers and Trends in the Size and Severity of Forest Fires Endangering WUI Areas: A Regional Case Study

This study explored, for the first time, the drivers shaping large fire size and high severity of forest fires classified as level-2 in Spain, which pose a great danger to the wildland–urban interface. Specifically, we examined how bottom-up (fuel type and topography) and top-down (fire weather) controls shaped level-2 fire behavior through a Random Forest classifier at the regional scale in Galicia (NW Spain). We selected for this purpose 93 level-2 forest fires. The accuracy of the RF fire size and severity classifications was remarkably high (>80%). Fire weather overwhelmed bottom-up controls in controlling the fire size of level-2 forest fires. The likelihood of large level-2 forest fires increased sharply with the fire weather index, but plateaued at values above 40. Fire size strongly responded to minimum relative humidity at values below 30%. The most important variables explaining fire severity in level-2 forest fires were the same as in the fire size, as well as the pre-fire shrubland fraction. The high-fire-severity likelihood of level-2 forest fires increased exponentially for shrubland fractions in the landscape above 50%. Our results suggest that level-2 forest fires will pose an increasing danger to people and their property under predicted scenarios of extreme weather conditions.

Climate change strongly affects future fire weather danger in Indian forests

Changes in temperature, precipitation, wind speed, and relative humidity due to climate change are likely to alter future fire regimes. We quantified the impact of such changes on the fire weather of Indian forests using a fire weather index and high-resolution downscaled climate projections. While conventional wisdom contends that future temperature increases will increase fire weather indices, we find this to be true only in dry forests. In humid forests, the fire weather index will decrease despite the warming due to future increases in precipitation and/or relative humidity. Days with severe fire weather danger will increase by up to 60% in dry forests but will reduce by up to 40% in humid forests. The fire season will be longer by 3–61 days across the country and the pre-monsoon fire season will become more intense over 55% of forests. This study suggests for countries like India with fragmented forests and diverse ecoclimates, standards and mitigation strategies must be developed at regional instead of national level.

Bundled climate-smart agricultural solutions for smallholder farmers in Sri Lanka

Smallholder farmers are among the most vulnerable to climate shocks in Sri Lanka. Lack of education and technical skills, poverty, risks inherent to agricultural investments, limited assets, and financial capital are major reasons for low investments in enhancing adaptive capacity. The study explores the use of agricultural technologies in improving smallholder resilience to water-related disasters and their opportunities for recovery. We tested four bundled services to promote climate-smart agriculture practices namely weather index insurance (WII), agronomic advisories dissemination via SMS, weather services, and climate-resilient seeds of maize and rice. The integrated solutions are referred to as Bundled Solutions of Index Insurance with Climate Information and Seed Systems (BICSA) to manage agricultural risks in Sri Lanka. The study conducted the bundled solutions in three agroecological regions spread over five districts and covering more than 2,500 farmers in three cropping periods ofMahaandYalaseasons. The results demonstrate that providing bundled solutions significantly protects smallholders against moderate drought events. The satellite-based weather index insurance can offset the long-term consequences of severe yield losses and mitigate the long-term drop in farm productivity. Our findings demonstrate the importance of bundled insurance to mitigate financial risks associated with extreme weather events and enhance resilience to climate change among vulnerable smallholders. It is evident from the study promoting a viable business model among seed companies, insurance companies, and technological partners, along with public institutions such as agricultural extension services can help production-level improvements and develop strategies at both the farm and policy levels that will support a transition to a more resilient farming system.

Farmers’ Willingness to Purchase Weather Index Crop Insurance: Evidence from Battambang, Cambodia

The weather index crop insurance (WICI) scheme was introduced under a pilot project for rice in Cambodia in 2021. The adoption rate was low and the loss ratio was higher than 200%. The increase in farmers’ participation would help reduce the loss ratio, which can sustain the WICI scheme. This study, therefore, examines Cambodian rice farmers’ willingness to purchase WICI in Cambodia. The hypothesis is that the low adoption rate is due to a lack of awareness, lack of understanding of WICI, lack of trust in weather stations, and the problem of basis risk. This study would like to test the influence of those factors on the willingness to purchase in Cambodia. Battambang Province was chosen as the study area as it is the largest area for rice production and has the largest take-up rate of farmers buying WICI. Detailed interviews of 232 farmers were conducted in the districts of Bavel and Moung Ruessei. The probit regression model was used to identify factors that significantly impact farmers’ willingness to purchase WICI. The results indicate that land size, level of trust in weather stations, level of farmers’ understanding of WICI, and joining the WICI awareness program have positive effects on the probability of farmers’ willingness to buy WICI, whereas the number of household laborers and expectation of floods have negative influences. The probability of willingness to purchase by farmers who attended the awareness program on WICI was 38% higher than those who did not. The size of farmland, level of trust in weather stations, and level of understanding of WICI increase in one unit affecting the probability of willingness to purchase WICI by 4%, 16%, and 25%, respectively. On the other hand, the increase in the number of household laborers in the rice field by one person and the increase in the probability of expected flood increase by 0.1 drag back the probability of farmers’ willingness to purchase by 16% and 5%, respectively. The results suggest the government to raise the insurance awareness and understanding of WICI. Development of weather station infrastructure, as well as maintenance of weather stations, is needed to guarantee the accuracy of data generated from the weather station.

Numerical solutions of an option pricing rainfall weather derivatives model

Weather derivatives are financial products used to cover non catastrophic weather events with a weather index as the underlying asset. In this work, we derive a rainfall weather derivative price model, based in the assumption that the rainfall dynamics follows a Ornstein-Uhlenbeck process. To calculate the price of the option we arrive at a two dimensional time dependent partial differential equation, where the spatial variables are the rainfall index and the total rainfall. Appropriate boundary conditions are suggested and they differ from the boundaries presented in literature in similar contexts. To compute the approximate solutions of the partial differential equation, we propose an explicit numerical method in order to deal efficiently with the different choices of the coefficients involved in the equation, that depend on the rainfall defice (or excess) and on the precipitation (amount of rain). Being an explicit numerical method, it will be conditionally stable and we discuss the stability region of the numerical method and its order of convergence. In the end we examine two test cases where the parameters of the model presented are estimated based on precipitation data.

Forest Fires, Stakeholders’ Activities, and Economic Impact on State-Level Sustainable Forest Management

In Europe, forest fires are a serious and constant threat. They destroy forests and forest land, causing damage, financial loss, and long-lasting impacts on forest ecosystem services. There are several ways to decrease the number of forest fires, including continuous investment in fire prevention measures and the intensive implementation of adaptive sustainable forest management measures, which need additional financial resources. In many cases, forest management activities in karst forests are not implemented in a timely manner and in coordination with other stakeholders. A comprehensive study about the impact of forest fires on different economic activities (tourism and protected areas) is not currently available. In this study, the legislative framework in Croatia was analysed in relation to the fire protection activities and jurisdictions of different institutions. From data collected in the period 2013–2020, the first-age class afforestation costs and growing stock assortment value were calculated, and the non-wood forest functions were estimated. The aforementioned data were further compared to the Fire Weather Index (FWI) and Seasonal Severity Rating (SSR) of fire seasons. The total estimated damage is EUR 326,810,724.72. The research emphasises the need for the implementation of cross-sectoral forest policy measures. The state forest company should allocate more financial resources for biological forest restoration in the future. Forest management practices should implement climate-adaptable silviculture measures to preserve forest and forest land.

A pricing model for agricultural insurance based on big data and machine learning

<p>Agricultural insurance is a crucial element of policies that promote and protect agriculture. It protects agriculture from risk and distributes agricultural hazards. The rural economy’s stabilization has been a significant stabilizer function. But as agriculture insurance has quickly advanced, a number of issues have unavoidably come to light. Agricultural insurance still offers a wide range of products and services available today. Big data will play a significant supporting role in the pressing need to innovate and improve goods and services. Other information supporting agricultural insurance includes agricultural data connected to it. The two previously most often utilized agricultural index insurances are regional yield insurance and weather index insurance. They struggle with risk pricing mostly due to a lack of appropriate empirical data, complicated dependence linkages between various hazards, and the prevalence of basis risk. A comprehensive study and review of pertinent research findings are carried out by modelling regional yield risk, building weather indicators and their distribution fitting, modelling agricultural dependence risk, and measuring and reducing basis risk. This article highlights the flaws in the current pricing models as well as the problems that need to be addressed in future studies. The need to further develop agricultural index insurance’s risk modelling techniques and increase the objectivity and precision of the pricing outcomes cannot be overstated in terms of their practical importance.</p>

Prediction of Ross River Virus Incidence Using Mosquito Data in Three Cities of Queensland, Australia.

Ross River virus (RRV) is the most common mosquito-borne disease in Australia, with Queensland recording high incidence rates (with an annual average incidence rate of 0.05% over the last 20 years). Accurate prediction of RRV incidence is critical for disease management and control. Many factors, including mosquito abundance, climate, weather, geographical factors, and socio-economic indices, can influence the RRV transmission cycle and thus have potential utility as predictors of RRV incidence. We collected mosquito data from the city councils of Brisbane, Redlands, and Mackay in Queensland, together with other meteorological and geographical data. Predictors were selected to build negative binomial generalised linear models for prediction. The models demonstrated excellent performance in Brisbane and Redlands but were less satisfactory in Mackay. Mosquito abundance was selected in the Brisbane model and can improve the predictive performance. Sufficient sample sizes of continuous mosquito data and RRV cases were essential for accurate and effective prediction, highlighting the importance of routine vector surveillance for disease management and control. Our results are consistent with variation in transmission cycles across different cities, and our study demonstrates the usefulness of mosquito surveillance data for predicting RRV incidence within small geographical areas.

Assessing the impact of climate change on future extreme temperature events in major South African cities

In this era of climate change, extreme weather events are expected to become more intense and frequent. This study analysed the long-term future climate data from the mean of five climate model intercomparison project phase 6 (CMIP6) global climate models (GCMs) to examine the impacts of climate change on extreme temperature in four major South African cities. The bias correction was successfully conducted using the CMhyd software program. The analysis of extreme temperatures was conducted using indices developed by the World Meteorological Organization’s Expert Team on Sector-specific Climate Indices (ET-SCI) and calculated using the R-based Climpact2 software. All statistical metrics (mean, R2 and RMSE) show that bias correction was fairly good, and further analysis and conclusions could also be drawn using the adjusted dataset. The overall result shows that annual trends of all temperature indices analysed in this study are significantly increasing for both scenarios (SSP2-4.5 and SSP5-8.5) except for some lower extreme temperature indices (i.e., number of cool days, cold nights and cold spells). In the historical time scale, however, some indices showed no trend for some stations. The study also found that coastal cities had a slower increase in higher extreme weather indices as compared to inland cities. However, for lower extreme indices (such as number of cool days, cold nights, cool day’s temperature and cold spells), the opposite was true. This information is important for policymakers, development agents and disaster prevention workers to make informed decisions about adapting to and mitigating extreme weather events.

Identifying the Drivers of Caribbean Severe Weather Impacts

Severe weather impacts in the central Caribbean are quantified by an objective index of daily maximum wind and rainfall (W•R) in the area 16–19°N, 63–69°W over the period 1970–2021. The index, based on ERA5 hindcast assimilation of satellite and in situ data, peaks from the July to October season as high sea temperatures and weak wind shear promote tropical cyclogenesis. Climate forcing is studied by reducing the W•R index to seasonal values and regressing the time series onto reanalysis fields 10°S–25°N, 180°W–20°E. The outcome reflects Jul–Oct warming in the tropical Atlantic, cooling in the tropical east Pacific (cold tongue), decreased/increased convection over the Pacific/Atlantic, and tropical upper easterly winds. New findings emerge in the Mar–Jun season preceding higher W•R: reduced SW-cloud bands in the northeast Pacific, a convective trough over the equatorial Atlantic, and Caribbean cold-air outbreaks. The multivariate El Niño Southern Oscillation index correlates with Jul–Oct Caribbean W•R at 2-month lead time and shows growing influence. Composite analysis of the top-10 years identifies an anomalous Pacific–Atlantic Walker Circulation favoring higher Caribbean W•R. Salinity is below normal and heat flux is downward across the Atlantic. Anomalous low-level airflow inhibits upwelling in the SW Caribbean, deepening atmospheric moisture. A leading case (TC Fiona 2022) demonstrates the environmental conditions underpinning storm intensification. The key drivers of severe weather impacts yield guidance in strategic planning, risk management and disaster preparedness. New insights are gained from a localized index of severe weather.

Analysis of Spatio-temporal Characteristics of Air Quality, Meteorological Impact, and Emission Reduction Effect During the Winter Olympics in Beijing-Tianjin-Hebei and Its Surrounding Areas

From January 31st to February 20th, 2015 to 2022, the spatio-temporal characteristics of the PM2.5 pollution evolution of 44 cities in the Beijing-Tianjin-Hebei Region and its surrounding areas were analyzed. The contribution of simultaneous meteorology, coordinated emission reduction, and cross-regional transmission to PM2.5 concentration and chemical component changes were quantified, respectively, with the aim to provide scientific reference for regional air quality joint prevention and control under adverse meteorological conditions. The results showed that the mean value of PM2.5 concentration of 44 cities in 2022 was the lowest(46 μg·m-3) without heavy pollution in the same period of the lunar calendar since 2015, whereas the proportion of days with good air quality was the highest(83.3%). PM2.5 pollution was more serious in the southern region than that in the northern region, and the high values were mainly concentrated along the Taihang Mountains and Yanshan transmission channel cities. In 2016, under the unregulated emission of fireworks in the Spring Festival, the proportion of unpolluted days was 93.5%, which means that the strong atmospheric diffusion ability was crucial to improve air quality. In 2022, the static weather index(SWI) increased by 2.1 compared with that in 2021, indicating unfavorable atmospheric diffusion capacity. The average and peak values of PM2.5 decreased by 14 μg·m-3 and 76 μg·m-3, respectively. The reduction in PM2.5 concentration in Beijing owing to emission reduction measures increased by 96% compared with that before one month. Under the adverse atmospheric background in the Shanxi-Shandong-Henan Region, which contributed to the increase in PM2.5 concentration, the peak value of PM2.5 concentration decreased by 87 μg·m-3, indicating that the peak clipping effect of regional collaborative pollution emission reduction was significant. From 2019 to 2022, the concentration of PM2.5 chemical components decreased yearly with narrowed spatial differences, and the high value was concentrated in the central and southern regions. The descending order of PM2.5 secondary component concentration decrease was as follows:organic matter(21.9%)>sulfate(20%)>ammonium salt(16.7%)>nitrate(9.7%). The proportion of nitrate in PM2.5 increased yearly to 30.1%, indicating that the contribution of traffic pollution was relatively prominent. In 2022, the SNA concentration in the Shanxi-Shandong-Henan Region increased. Pollution from external sources accounted for the main contribution in the Beijing-Tianjin-Hebei Region(>50%). Liaoning Province contributed significantly to the PM2.5 concentration in cities along the Bohai(>10%), and nitrate and sulfate were easy to transport over long distances. It is necessary to strengthen the coordinated control of precursors of secondary chemical components SO2, NOx, and NH3.