Preface

Following the success of 2014, 2016, 2018, and 2021 International Conference on Science & Technology Applications in Climate Change (STACLIM), the Institute of Climate Change (IPI), Universiti Kebangsaan Malaysia (UKM) is proud to extend our promotion of research and education for the advancement of climate change studies. The 2022 International Conference on Science & Technology Applications in Climate Change (STACLIM 2022) with the theme "Climate change mitigation action through the lens of science and technology” is the fifth in the series of conferences organized by IPI. This year the conference was carried out in virtual form through the Webex platform (29 – 30 November 2022) due to the COVID-19 travel restriction. Through the virtual form, the science community is able to share their research findings in time.The aim of this conference is to bring together researchers in fields of Environmental Science, Health Sustainability, Mathematics, Sustainable Energy, Economic Sustainability, Socio-Cultural Studies, Social Science, Atmospheric Science, and related fields, to present their research findings as well as create new opportunities for future research collaborations. This event is envisaged to witness active participation from various eminent environmental and earth scientists, engineers and students from academia, industry and government sectors for addressing complications associated with climate change and to draw forth novel and ground-breaking initiatives and solutions for climate resilience.The plenary sessions in the main room were opened by two keynote speeches from leading experts including Prof. Dr. Lisa Stein from University of Alberta, Canada on "Microbial Solutions to Mitigating Climate Change”, Prof. Dr. Haruko Kurihara from University of Ryukyus, Japan, on "Ocean acidification impacts on marine ecosystem and its potential mitigation solutions”. As the keynote session was open for public registration, we had participants joining the event. It was then followed by the invited speaker sessions consisting of Prof. Dr. Fredolin Tangang (UKM), Assoc. Prof. Dr. Rawshan Ara Begum (Macquarie University, Australia), Dr. Shantanu Kumar Pani (National Central University, Taiwan) and Mr. Saud Aldrees (University of Oxford, England). The program was then continued with oral presentation of 72 papers in 3 parallel breakout rooms. Each presenter was given up to 15 mins for presentation and Q&A sessions. There were additional 13 non-presenters who joined in during the presentation session. Presenters and participants have attended the conference from their respective countries including Malaysia, Indonesia, Philippines, USA, China, Saudi Arabia, Iraq, Algeria, India, and Ukraine.The conference went well with great support and synergy of the staff and personnel from Institute of Climate Change, UKM. To document and promulgate the research findings and ideas shared, we are very pleased to publish the accepted research papers of STACLIM 2022 in IOP Conference Series: Earth and Environmental Science (EES).The EditorsList of Organizing Committee is available in this Pdf.

Characteristics of PM2.5 and PM10 Spatio-Temporal Distribution and Influencing Meteorological Conditions in Beijing

PM2.5 and PM10 in the atmosphere seriously affect human health and air quality, a situation which has aroused widespread concern. In this paper, we analyze the temporal and spatial distribution of PM2.5 and PM10 concentrations from 2016 to 2021 based on real-time monitoring data. In addition, we also explore the influence of meteorological conditions on pollutants. The results show that PM2.5 and PM10 concentrations are similarly distribution in temporal and spatial from 2016 to 2021, and the average concentrations of both show a decreasing trend. The ratio of PM2.5 to PM10 is decreasing, indicating that the proportion of fine particles is declining. PM2.5 and PM10 concentrations are higher in spring and winter, but lower in summer. Spatially, it shows a gradual shift from the characteristic of "high in the south and low in the north" to a uniform homogenization across districts. The spatial distribution of PM2.5 and PM10 mass concentrations is synchronous by applying empirical orthogonal functions (EOF). The first EOF pattern exhibits a consistent characteristic of high in the southeast and low in the northwest. The second pattern EOF reflects the effect of impairing PM2.5 concentrations in the southeast during the winter of 2016-2018. The PM2.5 and PM10 concentrations are significantly negatively correlated with wind speed and precipitation in both spring and winter. On the other hand, from the perspective of the circulation situation, the southeasterly and weak westerly wind in spring produce convergence resulting in higher particulate matter concentrations in the south than in the north in Beijing. The westerly wind is flatter at 700 hPa geopotential height, which is conducive to the formation of stationary weather. The vertical direction of airflow in spring and winter is dominated by convergence and sinking, indicating the weak dispersion ability of the atmosphere. The reason for the accumulation of particulate matter at the surface is investigated, which is beneficial to provide the theoretical basis for air quality management and pollution control in Beijing.

Running a Scientific Conference During Pandemic Times

The October 2020 Virtual Symposium by the International Atmospheric Rivers Conference What: Despite the COVID-19 pandemic, the science of atmospheric rivers was well served by the organization of a virtual symposium joined by more than 100 researchers. In addition to conveying new science, significant lessons were learned on how to run virtual events. When: 5-9 October 2020 Where: Online. [ FROM AUTHOR] Copyright of Bulletin of the American Meteorological Society is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Observational data from uncrewed systems over Southern Great Plains

Uncrewed Systems (UxS), including uncrewed aerial systems (UAS) and tethered balloon/kite systems (TBS), are significantly expanding observational capabilities in atmospheric science. Rapid adaptation of these platforms and the advancement of miniaturized instruments have resulted in an expanding number of datasets captured under various environmental conditions by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. In 2021, observational data collected using ARM UxS platforms, including seven TigerShark UAS flights and 133 tethered balloon system (TBS) flights, were archived by the ARM Data Center (https://adc.arm.gov/discovery/#/, last access: 11 February 2022) and made publicly available at no cost for all registered users (10.5439/1846798) (Mei and Dexheimer, 2022). These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research. This paper describes the DOE UAS/TBS datasets, including information on the acquisition, collection, and quality control processes, and highlights the potential scientific contributions using UAS and TBS platforms.

COVID-19 Lockdown: Impact on PM10 and PM2.5 in Six Megacities in the World Assessed Using NASA's MERRA-2 Reanalysis

The changes in air quality were investigated in six megacities during the shutdown phases in 2020 and were compared to the same time periods in the previous 10 years (2010-2019) using the data of Modern-Era Retrospective Analysis and Research and Application, version 2 (MERRA-2). The concentrations of PM10 and PM2.5 were greatly reduced in all megacities during the lockdown in 2020 when compared to the same period in 2019 and in the previous ten years. The highest reduction in PM10 was recorded in Delhi, and Sao Paulo (21%, and 15% and by 27%, and 9%), when compared with the concentrations in 2019 and in the period 2010-2019, respectively. Similarly, levels of PM2.5 in Delhi, Sao Paulo, Beijing, and Mumbai decreased by 20%, 14%, 12%, and 10%, respectively in 2020 when compared to the last ten years. Results indicated that the lockdown is an effective mitigation measure to improve air quality. The MERRA-2 reanalysis dataset could be a vital tool in air quality studies in places with a lack of In-situ observations.

Effect of Reduced Emissions from Thermal Power Plants in China on Local Air Quality Improvement

In September 2021, China encountered the shortage of coal and emission activity shrunk to the lowest it had been since February 2020, when coronavirus lockdowns paralyzed the economy. Despite social and economic concerns, this event has brought unexpected positive consequences for environmental quality. In this study, we investigated the impact of restricted thermal power plants operations on local air quality is investigated. For this purpose, social statistics, satellite observations, ground-based air quality measurements, and meteorology data were analyzed to estimate the spatio-temporal characteristics of air pollutants. As a result, significant reductions in the concentrations of air pollutants were found. It was about 11.13% lower than the reduced concentration due to the coronavirus lockdowns period in China. Compared to the average value during the normal year for 2015-2019, large decreased values of SO2 (-38.81%), CO (-24.78%), NO2 (-54.26%), PM 10 (-51.60%), and PM2.5 (-56.35%) were measured in Seoul, Korea. Satellite observation data confirm that these large drop in air pollution levels occurred in both countries, serving as a valuable proof of the emission control impact on local air quality. Moreover, the easterly wind along with the increased precipitation rate in China could affect the wet deposition and dilution of air pollutants.

Photochemical sensitivity to emissions and local meteorology in Bogota, Santiago, and Sao Paulo: An analysis of the initial COVID-19

This study delves into the photochemical atmospheric changes reported globally during the pandemic by analyzing the change in emissions from mobile sources and the contribution of local meteorology to ozone (O-3) and particle formation in Bogota (Colombia), Santiago (Chile), and Sao Paulo (Brazil). The impact of mobility reductions (50%-80%) produced by the early coronavirus-imposed lockdown was assessed through high-resolution vehicular emission inventories, surface measurements, aerosol optical depth and size, and satellite observations of tropospheric nitrogen dioxide (NO2) columns. A generalized additive model (GAM) technique was also used to separate the local meteorology and urban patterns from other drivers relevant for O-3 and NO2 formation. Volatile organic compounds, nitrogen oxides (NOx), and fine particulate matter (PM2.5) decreased significantly due to motorized trip reductions. In situ nitrogen oxide median surface mixing ratios declined by 70%, 67%, and 67% in Bogota, Santiago, and Sao Paulo, respectively. NO2 column medians from satellite observations decreased by 40%, 35%, and 47%, respectively, which was consistent with the changes in mobility and surface mixing ratio reductions of 34%, 25%, and 34%. However, the ambient NO2 to NOx ratio increased, denoting a shift of the O-3 formation regime that led to a 51%, 36%, and 30% increase in the median O-3 surface mixing ratios in the 3 respective cities. O-3 showed high sensitivity to slight temperature changes during the pandemic lockdown period analyzed. However, the GAM results indicate that O-3 increases were mainly caused by emission changes. The lockdown led to an increase in the median of the maximum daily 8-h average O-3 of between 56% and 90% in these cities.

How Is COVID-19 Affected by Weather? Metaregression of 158 Studies and Recommendations for Best Practices in Future Research

Because many viral respiratory diseases show seasonal cycles, weather conditions could affect the spread of coronavirus disease 2019 (COVID-19). Although many studies pursued this possible link early in the pandemic, their results were inconsistent. Here, we assembled 158 quantitative empirical studies examining the link between weather and COVID-19. A metaregression analysis was performed on their 4793 correlation coefficients to explain these inconsistent results. We found four principal findings. First, 80 of the 158 studies did not state the time lag between infection and reporting, rendering these studies ineffective in determining the weather-COVID-19 relationship. Second, the research outcomes depended on the statistical analysis methods employed in each study. Specifically, studies using correlation tests produced outcomes that were functions of the geographical locations of the data from the original studies, whereas studies using linear regression produced outcomes that were functions of the analyzed weather variables. Third, Asian countries had more positive associations for air temperature than other regions, possibly because the air temperature was undergoing its seasonal increase from winter to spring during the rapid outbreak of COVID-19 in these countries. Fourth, higher solar energy was associated with reduced COVID-19 spread, regardless of statistical analysis method and geographical location. These results help to interpret the inconsistent results and motivate recommendations for best practices in future research. These recommendations include calculating the effects of a time lag between the weather and COVID-19, using regression analysis models, considering nonlinear effects, increasing the time period considered in the analysis to encompass more variety of weather conditions and to increase sample size, and eliminating multicollinearity between weather variables.

Nationwide CoViD-19 lockdown impact on air quality in India

This paper presents the comparative results of surface and satellite measurements made during the Phase 1 (25 March to 14 April), Phase 2 (15 April to 3 May) and Phase 3 (3 May to 17 May) of Covid-19 imposed lockdown periods of 2020 and those of the same locations and periods during 2019 over India. These comparative analyses are performed for Indian states and Tier 1 megacities where economic activities have been severely affected with the nationwide lockdown. The focus is on changes in the surface concentration of sulfur dioxide (SO2), carbon monoxide (CO), PM2.5 and PM10, Ozone (O-3), Nitrogen dioxide (NO2) and retrieved columnar NO2 from TROPOMI and Aerosol Optical Depth (AOD) from MODIS satellite. Surface concentrations of PM2.5 were reduced by 30.59%, 31.64% and 37.06%, PM10 by 40.64%, 44.95% and 46.58%, SO2 by 16.73%, 12.13% and 6.71%, columnar NO2 by 46.34%, 45.82% and 39.58% and CO by 45.08%, 41.51% and 60.45% during lockdown periods of Phase 1, Phase 2 and Phase 3 respectively as compared to those of 2019 periods over India. During 1st phase of lockdown, model simulated PM2.5 shows overestimations to those of observed PM2.5 mass concentrations. The model underestimates the PM2.5 to those of without reduction before lockdown and 1st phase of lockdown periods. The reduction in emissions of PM2.5, PM10, CO and columnar NO2 are discussed with the surface transportation mobility maps during the study periods. Reduction in the emissions based on the observed reduction in the surface mobility data, the model showed excellent skills in capturing the observed PM2.5 concentrations. Nevertheless, during the 1st & 3rd phases of lockdown periods AOD reduced by 5 to 40%. Surface O-3 was increased by 1.52% and 5.91% during 1st and 3rd Phases of lockdown periods respectively, while decreased by-8.29% during 2nd Phase of lockdown period.

Reduced Probability of 2020 June July Persistent Heavy Mei-yu Rainfall Event in the Middle to Lower Reaches of the Yangtze River Basin under Anthropogenic Forcing

Anthropogenic forcing has approximately halved the probability of 2020 June-July persistent heavy mei-yu rainfall event based on HadGEM3-GA6 simulations without considering the COVID-induced aerosol emission reduction.

An Analysis of Virtual Research Experiences for Undergraduates Programs in Light of the COVID-19 Pandemic

With the continued social distancing requirements of the novel COVID-19 pandemic, many in-person educational programs were halted in 2020, including specialty education and research experiences for undergraduates. However, some Research Experiences for Undergraduates (REUs) progressed in summer 2020 in a fully virtual format. The importance of understanding how these practical STEM skills translated in a virtual REU format, in addition to areas of improvement going forward, are critical to the development of effective online STEM learning through REUs. Two survey instruments were designed to capture data from both the REU mentors (including the PIs) and the students in the programs. Questions included information on the REU they participated in, their perceptions of the best and worst aspects, their overall satisfaction with the experience, and their likelihood to seek out virtual REUs in the future. Overall, both students and faculty involved in virtual REUs were glad to have had the experience and were satisfied with it. The benefits of flexibility, the ease of communication and scheduling, and the increased access to online resources were echoed as the strengths of the virtual format. However, many believe that an in-person REU had benefits that could not be replicated in a virtual environment, including community building and hands-on experiences. Several were bogged down by technical difficulties. With more effort made to include community building to a greater extent, as well as considerations and planning for technical demands, the future of widely accessible online REU experiences is a bright one.

AiR: An Augmented Reality Application for Visualizing Air Pollution

In order to effectively combat Air Pollution, it is necessary for the government and the community to work together. Easily comprehensible visualizations can play a major role in drawing public attention and spreading awareness about seemingly intangible air pollution. Considering the widespread usage of Android-based devices, in this paper, we propose an Augmented Reality based application called AiR, to help users to visualize pollutants in the air and to create an immersive user experience. It aims to interactively engage a wide variety of users and create awareness without overwhelming them with data. AiR visualizes 12 pollutants [PM10, PM2.5, NO, NO2, NOx, CO, SO2,O-3,NH3, C6H6, (CH3)C6H5 and (CH3)(2)C6H5] through unique models. We demonstrate our application on pollution data by CPCB from various weather stations across India collected over the initial lockdown period due to COVID-19 in India.

Quantification of methane emissions from hotspots and during CUVID-19 using a global atmospneric inversion

Concentrations of atmospheric methane (CH4), the second most important greenhouse gas, continue to grow. In recent years this growth rate has increased further (2020: +15.6 ppb), the cause of which remains largely unknown. Here, we demonstrate a high-resolution (similar to 80 km), short-window (24 h) 4D-Var global inversion system based on the ECMWF Integrated Forecasting System (IFS) and newly available satellite observations. The largest national disagreement found between prior (5.3 Tg per month) and posterior (5.0 Tg per month) CH4 emissions is from China, mainly attributed to the energy sector. Emissions estimated from our global system are in good agreement with those of previous regional studies and point source-specific studies Emission events (leaks or blowouts) > 10 t CH(4)h(-1) were detected, but without appropriate prior uncertainty information, were not well quantified. Our results suggest that global anthropogenic CH4 emissions for the first 6 months of 2020 were, on average, 470 Gg per month (+1.6 %) higher than for 2019, mainly attributed to the energy and agricultural sectors. Regionally, the largest increases were seen from China (+220 Gg per month, 4.3 %), with smaller increases from India (+50 Gg per month, 1.5 %) and the USA (+40 Gg per month, 2.2 %). When assuming a consistent year-on-year positive trend in emissions, results show that during the onset of the global slowdown (March-April 2020) energy sector CH4 emissions from China increased above expected levels;however, during later months (May-June 2020) emissions decreased below expected levels. Results for the first 6 months of 2019/20 suggest that the accumulated impact of the COVID-19 slowdown on CH4 emissions from March-June 2020 might be small relative to the long-term positive trend in emissions. Changes in OH concentration, not investigated here, may have contributed to the observed growth in 2020.

Pandemic shows that global climate and biodiversity science assessments need to be annual

The coronavirus disease 2019 (COVID-19) pandemic has demonstrated that with daily data informing citizens and governments of the situation, society can respond to a crisis rapidly. We suggest that the current multi-yearly assessments of the, by comparison, chronic crises of climate change and biodiversity loss are no longer fit for purpose. Instead, annual assessments (as is standard for governmental budgets, commercial and personal taxes) are now required. As with COVID-19 data, the annual progress reports proposed here must be accompanied by new biodiversity and climate data that are both timely and open access, thus supporting scientific advice. With public transparency and accessibility of facts and figures, annual reports will lead to more accountability and progress in addressing the causes of these crises.

Estimation of the SARS-CoV-2 Virus Inactivation Time Using Spectral Ultraviolet Radiation

Corona Virus Disease 19 pandemic (COVID-19) causes many deaths worldwide, and has enormous impacts on society and economy. The COVID-19 was caused by a new type of coronavirus (Severe Acute Respiratory Syndrome Cornonavirus 2;SARS-CoV-2), which has been found that these viruses can be effectively inactivated by ultraviolet (UV) radiation of 290315 nm. In this study, 90% inactivation time of the SARS-CoV-2 virus was analyzed using ground observation data from Brewer spectrophotometer at Yonsei University, Seoul and simulation data from UVSPEC for the period of 2015-2017 and 2020. Based on 12:00-13:00 noon time, the shortest virus inactivation time were estimated as 13.5 minutes in June and 4.8 minutes in July/August, respectively, under all sky and clear sky conditions. In the diurnal and seasonal variations, SARS-CoV-2 could be inactivated by 90% when exposed to UV radiation within 60 minutes from 10:00 to 14:00, for the period of spring to autumn. However, in winter season, the natural prevention effect was meaningless because the intensity of UV radiation weakened, and the time required for virus inactivation increased. The spread of infectious diseases such as COVID-19 is related to various and complex interactions of several variables, but the natural inactivation of viruses by UV radiation presented in this study, especially seasonal differences, need to be considered as major variables.

Coughing Intensity and Wind Direction Effects on the Transmission of Respiratory Droplets: A Computation with Euler-Lagrange Method

Studies on droplet transmission are needed to understand the infection mechanism of SARS-CoV-2. This research investigated the effects of coughing intensity and wind direction on respiratory droplets transportation using the Euler-Lagrange method. The results revealed that both coughing intensity and wind conditions considerably influence the transmission of small and medium droplets but had little effect on large droplets. A stronger coughing intensity resulted in small and medium droplets traveling farther in a calm wind and spreading widely and rapidly in a windy environment. The droplets do not travel far in the absence of ambient wind, even with stronger coughing. Medium droplets spread in clusters, and small droplets drifted out of the domain in the band area in different wind conditions except for 60 degrees and 90 degrees wind directions, in which cases, the droplets were blown directly downstream. In 0 degrees wind direction, many droplets were deposited on the human body. The fast and upward movement of particles in 60 degrees and 90 degrees directions could cause infection risk with short exposure. In 180 degrees wind direction, droplets spread widely and traveled slowly because of the reverse flow downstream, prolonged exposure can result in a high risk of infection.

Ozone Trends and the Ability of Models to Reproduce the 2020 Ozone Concentrations in the South Coast Air Basin in Southern California under the COVID-19 Restrictions

The current U.S. emission control requirements for on-road motor vehicles are driven by the ozone problem in the South Coast Air Basin (SoCAB) in southern California. Based on ozone modeling performed for Air Quality Management Plans (AQMPs), the SoCAB ozone attainment plan requires large (>80%) amounts of emission reductions in oxides of nitrogen (NOx) from current levels with more modest (similar to 40%) controls on Volatile Organic Compounds (VOC). The shelter in place orders in response to the 2020 COVID-19 pandemic resulted in an immediate reduction in emissions, but instead of ozone being reduced, in 2020 the SoCAB saw some of the highest observed ozone levels in decades. We used the abrupt emissions reductions from 2019 to 2020 caused by COVID-19 to conduct a dynamic model evaluation of the Community Multiscale Air Quality (CMAQ) model to evaluate whether the models used to develop ozone control plans can correctly simulate the ozone response to the emissions reductions. Ozone modeling was conducted for three scenarios: 2019 Base, 2020 business-as-usual (i.e., without COVID reductions), and 2020 COVID. We found that modeled ozone changes between 2019 and 2020 were generally consistent with the observed ozone changes. We determined that meteorology played the major role in the increases in ozone between 2019 and 2020;however, the reduction in NOX emissions also caused ozone increases in Los Angeles County and into western San Bernardino County, with more widespread ozone decreases further to the east.

NOTES AND CORRESPONDENCE Did Atmospheric CO2 and CH4 Observation at Yonagunijima Detect Fossil-Fuel CO2 Reduction due to COVID-19 Lockdown?

Synoptic-scale variabilities of atmospheric CO2 and CH4 observed at Yonagunijima (Yonaguni Island, YON, 24.47 degrees N, 123.01 degrees E) during winter (from January to March) in 1998 - 2020 were examined. The monthly mean variability ratios (Delta CO2 /Delta CH4) based on correlation slopes within 24 h time windows showed a clear increasing trend, which is mainly attributed to the unprecedented increase in the fossil fuel-derived CO2 (FFCO2) emissions from China. A similar increasing trend of the Delta CO2 /Delta CH4 ratio had been reported for the observation at Hateruma Island (HAT, 24.06 degrees N, 123.81 degrees E), located at approximately 100 km east of YON. Nevertheless, the absolute values for YON were 34 % larger than those for HAT. Additionally, the monthly average in February 2020 for YON showed no marked change, whereas that for HAT showed an abrupt considerable decrease associated with the FFCO2 emission decrease in China presumably caused by the COVID-19 lockdown. Investigating the diurnal variations, we found that the local influences were larger at YON, especially during daytime, than at HAT. Using nighttime data (20-6 LST) and a longer time window (84 h), we succeeded in reducing the local influences and the resulting monthly mean Delta CO2 /Delta CH4 ratio showed considerable similarity to that observed at HAT including the abrupt decrease in February 2020. These results convinced us that the Delta CO2 /Delta CH4 ratio could be successfully used to investigate the relative emission strength in the upwind region.

Spatio-Temporal Characteristics of Air Quality Index (AQI) over Northwest China

In recent years, air pollution has become a serious threat, causing adverse health effects and millions of premature deaths in China. This study examines the spatial-temporal characteristics of ambient air quality in five provinces (Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX), and Qinghai (QH)) of northwest China (NWC) from January 2015 to December 2018. For this purpose, surface-level aerosol pollutants, including particulate matter (PMx, x = 2.5 and 10) and gaseous pollutants (sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O-3)) were obtained from China National Environmental Monitoring Center (CNEMC). The results showed that fine particulate matter (PM2.5), coarse particulate matter (PM10), SO2, NO2, and CO decreased by 28.2%, 32.7%, 41.9%, 6.2%, and 27.3%, respectively, while O-3 increased by 3.96% in NWC during 2018 as compared with 2015. The particulate matter (PM2.5 and PM10) levels exceeded the Chinese Ambient Air Quality Standards (CAAQS) Grade II standards as well as the WHO recommended Air Quality Guidelines, while SO2 and NO2 complied with the CAAQS Grade II standards in NWC. In addition, the average air quality index (AQI), calculated from ground-based data, improved by 21.3%, the proportion of air quality Class I (0-50) improved by 114.1%, and the number of pollution days decreased by 61.8% in NWC. All the pollutants' (except ozone) AQI and PM2.5/PM10 ratios showed the highest pollution levels in winter and lowest in summer. AQI was strongly positively correlated with PM2.5, PM10, SO2, NO2, and CO, while negatively correlated with O-3. PM10 was the primary pollutant, followed by O-3, PM2.5, NO2, CO, and SO2, with different spatial and temporal variations. The proportion of days with PM2.5, PM10, SO2, and CO as the primary pollutants decreased but increased for NO2 and O-3. This study provides useful information and a valuable reference for future research on air quality in northwest China.