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In this study, an analysis framework based on the regular monitoring data was proposed for investigating the annual/inter-annual air quality variation and the contributions from different factors (i.e., seasons, pollution periods and airflow directions), through a case study in Beijing from 2013 to 2016. The results showed that the annual mean concentrations (MC) of PM2.5, SO2, NO2 and CO had decreased with annual mean ratios of 7.5%, 28.6%, 4.6% and 15.5% from 2013 to 2016, respectively. Among seasons, the MC in winter contributed the largest fractions (25.8%~46.4%) to the annual MC, and the change of MC in summer contributed most to the inter-annual MC variation (IMCV) of PM2.5 and NO2. For different pollution periods, gradually increase of frequency of S-1 (PM2.5, 0 ~ 75 μg/m3) made S-1 become the largest contributor (28.8%) to the MC of PM2.5 in 2016, it had a negative contribution (− 13.1%) to the IMCV of PM2.5; obvious decreases of frequencies of heavily polluted and severely polluted dominated (44.7% and 39.5%) the IMCV of PM2.5. For different airflow directions, the MC of pollutants under the south airflow had the most significant decrease (22.5%~62.5%), and those decrease contributed most to the IMCV of PM2.5 (143.3%), SO2 (72.0%), NO2 (55.5%) and CO (190.3%); the west airflow had negative influences to the IMCV of PM2.5, NO2 and CO. The framework is helpful for further analysis and utilization of the large amounts of monitoring data; and the analysis results can provide scientific supports for the formulation or adjustment of further air pollution mitigation policy.

Accurate identification of source parameters (source strength and location) of sudden air pollution accidents (SAPAs) is important for implementation of adequate responses. However, the potential impact of atmospheric diffusion conditions on source parameter identification may be significant. An inversion model that combines the hybrid particle swarm optimization and the Nelder–Mead simplex search method (PSO-NM) with the Gaussian dispersion model was proposed to identify the source parameters and to investigate the influences of different atmospheric conditions on the identifications. A case study based on 68 SO2 leakage tests from the Prairie Grass field experiment was conducted. The source strengths and locations of the 68 tests were estimated by the combined inversion model. The results indicated that the inversion model can effectively get accurate and robust source parameter estimations. The average absolute value of relative deviation of source strength was 13.8% ± 11.4%; the average absolute deviations for parameters x0, y0, z0 and the total distances were 18.9 ± 36.9 m, 2.7 ± 5.2 m, 3.5 ± 9.7 m and 19.6 ± 38.1 m, respectively. A comprehensive evaluation method was also proposed for analyzing the impacts of atmospheric conditions on source parameter estimations. The results showed that the source parameter estimations under atmospheric stability classes E and C have the best accuracy and robustness, followed by stability classes A and D; while the worst occurred under atmospheric stability classes B and F. The analysis results can provide scientific support for the formulation or adjustment of emergency response strategies used in sudden air pollution accidents. The new inversion model proposed is a supplement to the methodology of inversing source parameters.

Accurate and rapid predictions of air pollutant dispersion are important for effective emergency responses after sudden air pollution accidents (SAPA). Notably, dispersion parameters (σ) are the key variables that influence the simulation accuracy of dispersion models. Empirical dispersion schemes based on power-law formulas are probably appropriate choices for simulations in SAPA because of the requirement for only routine meteorological data. However, performance comparisons of different schemes are lacking. In this study, the performances during simulations of air pollutant dispersion of four typical empirical parameterised schemes, i.e. BRIGGS, SMITH, Pasquill-Gifford, and Chinese National Standard (CNS), were investigated based on the GAUSSIAN plume model with datasets for the classic Prairie Grass experiments, 1956. The performances when simulating peak and overall concentrations in different Pasquill atmospheric stability classes (A, B, C, D, E, F) were quantitatively analysed through different statistical approaches. Results showed that the performances of four schemes for peak and overall concentrations were basically consistent. Scheme CNS in unstable atmospheric conditions (A, B, and C) performed significantly better than the others according to performance criteria, which included the lowest mean of absolute value of fractional biases, lowest normalised mean square errors, and largest mean values of the fraction within a factor of two when predicting peak and overall concentrations, respectively. Schemes BRIGGS and P-G exhibited slightly better performances during the neutral condition (D) followed by scheme CNS. Schemes SMITH and CNS demonstrated slight merits in predicting concentrations compared to the other schemes during stable conditions (E and F). As a whole, scheme CNS generally performed well for the different atmospheric stability classes. These analysis results can help to fill in the data gaps and improve our understanding of the influence of typical power-law function schemes on simulations of air pollutant dispersion. The results are expected to provide scientific support for air pollution predictions, especially during emergency responses to SAPA.

Air pollutant distributions identified through monitoring form an important basis for devising adequate emergency responses against sudden air pollution accidents (SAPAs). However, studies on the optimisation of monitoring layouts for SAPAs, which can help us better understand air pollutant dispersion, are lacking. We developed an emergency monitoring layout optimisation method for SAPAs that combines an air pollutant dispersion model, a fuzzy comprehensive evaluation method (FCEM), and a post-optimality analysis technique (POAT) to identify the optimal monitoring layout scheme based on a field sulphur hexafluoride (SF6) leakage test. The impact of the number of monitoring sites on the results (coverage rate and accuracy) were investigated and analysed through a proposed comprehensive evaluation method. A monitoring result–monitoring number curve was plotted to determine the optimal number of monitoring sites. Two scenarios, i.e. with and without environmentally sensitive monitoring sites, were analysed. The results showed that the developed optimisation method can effectively consider the role of sensitive sites and obtain a feasible network for monitoring SAPAs. The coverage rates of the optimal monitoring layout schemes were 76.2% and 61.9% for the two case scenarios, respectively; the relative deviations of mean air pollutant concentrations at the sites were 14.6% and −12.2%, respectively. We investigated the effect of incorporating POAT into the method by comparing the optimal emergency monitoring layout schemes based on dispersion model–FCEM and model–FCEM–POAT. The results showed that the coverage rates of the optimised monitoring layout schemes improved when POAT was introduced. The proposed method can help determine an adequate, optimised emergency monitoring network for SAPAs.