Severe Global Environmental Issues Caused by Canada’s Record-Breaking Wildfires in 2023

Due to the record-breaking wildfires that occurred in Canada in 2023,unprecedented quantities of air pollutants and greenhouse gases were released into the atmosphere.The wildfires had emitted more than 1.3 Pg CO_(2)and 0.14 Pg CO_(2)equivalent of other greenhouse gases(GHG)including CH4 and N_(2)O as of 31 August.The wildfire-related GHG emissions constituted more than doubled Canada’s planned cumulative anthropogenic emissions reductions in 10 years,which represents a significant challenge to climate mitigation efforts.The model simulations showed that the Canadian wildfires impacted not only the local air quality but also that of most areas in the northern hemisphere due to long-range transport,causing severe PM_(2.5)pollution in the northeastern United States and increasing daily mean PM_(2.5)concentration in northwestern China by up to 2μg m-3.The observed maximum daily mean PM_(2.5)concentration in New York City reached 148.3μg m-3,which was their worst air quality in more than 50 years,nearly 10 times that of the air quality guideline(i.e.,15μg m-3)issued by the World Health Organization(WHO).Aside from the direct emissions from forest fires,the peat fires beneath the surface might smolder for several months or even longer and release substantial amounts of CO_(2).The substantial amounts of greenhouse gases from forest and peat fires might contribute to the positive feedback to the climate,potentially accelerating global warming.To better understand the comprehensive environmental effects of wildfires and their interactions with the climate system,more detailed research based on advanced observations and Earth System Models is essential.

Investigating the Transport Mechanism of PM2.5 Pollution during January 2014 in Wuhan, Central China

Severe haze pollution that occurred in January 2014 in Wuhan was investigated. The factors leading to Wuhan’s PM2.5 pollution and the characteristics and formation mechanism were found to be significantly different from other megacities, like Beijing. Both the growth rates and decline rates of PM2.5 concentrations in Wuhan were lower than those in Beijing, but the monthly PM2.5 value was approximately twice that in Beijing. Furthermore, the sharp increases of PM2.5 concentrations were often accompanied by strong winds. A high-precision modeling system with an online source-tagged method was established to explore the formation mechanism of five haze episodes. The long-range transport of the polluted air masses from the North China Plain (NCP) was the main factor leading to the sharp increases of PM2.5 concentrations in Wuhan, which contributed 53.4% of the monthly PM2.5 concentrations and 38.5% of polluted days. Furthermore, the change in meteorological conditions such as weakened winds and stable weather conditions led to the accumulation of air pollutants in Wuhan after the long-range transport. The contribution from Wuhan and surrounding cities to the PM2.5 concentrations was determined to be 67.4% during this period. Under the complex regional transport of pollutants from surrounding cities, the NCP, East China, and South China, the five episodes resulted in 30 haze days in Wuhan. The findings reveal important roles played by transregional and intercity transport in haze formation in Wuhan.

Investigating the Changes in Air Pollutant Emissions over the Beijing-Tianjin-Hebei Region in February from 2014 to 2019 through an Inverse Emission Method

In recent years,China has implemented several measures to improve air quality.The Beijing-Tianjin-Hebei(BTH)region is one area that has suffered from the most serious air pollution in China and has undergone huge changes in air quality in the past few years.How to scientifically assess these change processes remain the key issue in further improving the air quality over this region in the future.To evaluate the changes in major air pollutant emissions over this region,this paper employs ensemble Kalman filtering(EnKF)for integrating the national ground monitoring pollutant observation data and the Nested Air Quality Prediction Modeling System(NAQPMS)simulation data to inversely estimate the emission rates of SO_(2),NOX,CO,and primary PM_(2.5)over BTH region in February from 2014 to 2019.The results show that SO_(2),NOX,CO,and primary PM_(2.5)emissions in the BTH region decreased in February from 2014 to 2019 by 83%,37%,41%,and 42%,while decreases in Beijing during this period were 86%,67%,59%,and 65%,respectively.Compared with the prior emission inventory,the inversion emission inventory reduces the uncertainty of multi-pollutant simulation in the BTH region,with simulated root mean square errors of the monthly average concentrations of SO_(2),NOX,PM_(2.5),and CO reduced by 41%,30%,31%,and 22%,respectively.The average uncertainties of SO_(2),NOX,PM_(2.5),and CO inversion emissions in2014-19 are±14.03%yr^(-1),±28.91%yr^(-1),±126.15%yr^(-1),and±43.58%yr^(-1).Compared with the uncertainty of MEIC emission,the uncertainties of all species changed by+2%yr^(-1),-2%yr^(-1),-26%yr^(-1),and-4%yr^(-1),respectively.The spatial distribution results illustrate that air pollutant emissions are mainly distributed over the eastern and southern BTH regions.The spatial gap between the inversion emissions and MEIC emissions was further closed in 2019 compared to 2014.The results of this paper can provide a new reference for assessing changes in air pollution emissions over the BTH region in recent years and validating a bottom-up emission inventory.

Probabilistic Automatic Outlier Detection for Surface Air Quality Measurements from the China National Environmental Monitoring Network

Although quality assurance and quality control procedures are routinely applied in most air quality networks, outliers can still occur due to instrument malfunctions, the influence of harsh environments and the limitation of measuring methods. Such outliers pose challenges for data-powered applications such as data assimilation, statistical analysis of pollution characteristics and ensemble forecasting. Here, a fully automatic outlier detection method was developed based on the probability of residuals, which are the discrepancies between the observed and the estimated concentration values. The estimation can be conducted using filtering—or regressions when appropriate—to discriminate four types of outliers characterized by temporal and spatial inconsistency, instrument-induced low variances, periodic calibration exceptions, and less PM_(10) than PM_(2.5) in concentration observations, respectively. This probabilistic method was applied to detect all four types of outliers in hourly surface measurements of six pollutants(PM_(2.5), PM_(10),SO_2,NO_2,CO and O_3) from 1436 stations of the China National Environmental Monitoring Network during 2014-16. Among the measurements, 0.65%-5.68% are marked as outliers. with PM_(10) and CO more prone to outliers. Our method successfully identifies a trend of decreasing outliers from 2014 to 2016,which corresponds to known improvements in the quality assurance and quality control procedures of the China National Environmental Monitoring Network. The outliers can have a significant impact on the annual mean concentrations of PM_(2.5),with differences exceeding 10 μg m^(-3) at 66 sites.

The Impact of the Numbers of Monitoring Stations on the National and Regional Air Quality Assessment in China During 2013-18

China national air quality monitoring network has become the core data source for air quality assessment and management in China.However,during network construction,the significant change in numbers of monitoring sites with time is easily ignored,which brings uncertainty to air quality assessments.This study aims to analyze the impact of change in numbers of stations on national and regional air quality assessments in China during 2013-18.The results indicate that the change in numbers of stations has different impacts on fine particulate matter(PM_(2.5))and ozone concentration assessments.The increasing number of sites makes the estimated national and regional PM_(2.5) concentration slightly lower by 0.6−2.2μg m^(−3) and 1.4−6.0μg m^(−3) respectively from 2013 to 2018.The main reason is that over time,the monitoring network expands from the urban centers to the suburban areas with low population densities and pollutant emissions.For ozone,the increasing number of stations affects the long-term trends of the estimated concentration,especially the national trends,which changed from a slight upward trend to a downward trend in 2014−15.Besides,the impact of the increasing number of sites on ozone assessment exhibits a seasonal difference at the 0.05 significance level in that the added sites make the estimated concentration higher in winter and lower in summer.These results suggest that the change in numbers of monitoring sites is an important uncertainty factor in national and regional air quality assessments,that needs to be considered in long-term concentration assessment,trend analysis,and trend driving force analysis.

A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions:A case study in the Pearl River Delta,China

The conventional Ensemble Kalman filter(EnKF),which is now widely used to calibrate emission inventories and to improve air quality simulations,is susceptible to simulation errors of meteorological inputs,making accurate updates of high temporal-resolution emission inventories challenging.In this study,we developed a novel meteorologically adjusted inversion method(MAEInv)based on the EnKF to improve daily emission estimations.The new method combines sensitivity analysis and bias correction to alleviate the inversion biases caused by errors of meteorological inputs.For demonstration,we used the MAEInv to inverse daily carbon monoxide(CO)emissions in the Pearl River Delta(PRD)region,China.In the case study,60%of the total CO simulation biases were associated with sensitive meteorological inputs,which would lead to the overestimation of daily variations of posterior emissions.Using the new inversion method,daily variations of emissions shrank dramatically,with the percentage change decreased by 30%.Also,the total amount of posterior CO emissions estimated by the MAEInv decreased by 14%,indicating that posterior CO emissions might be overestimated using the conventional EnKF.Model evaluations using independent observations revealed that daily CO emissions estimated by MAEInv better reproduce the magnitude and temporal patterns of ambient CO concentration,with a higher correlation coefficient(R,+37.0%)and lower normalized mean bias(NMB,-17.9%).Since errors of meteorological inputs are major sources of simulation biases for both low-reactive and reactive pollutants,the MAEInv is also applicable to improve the daily emission inversions of reactive pollutants.