In this paper we derive in great detail the formula for count rates of gamma-ray bursts (GRBs) in the framework of fireballs, in terms of the integral of time, where the Doppler effect of the expanding fireball surface is the key factor concerned. Effects arising from the limit on the time delay due to the limited emitting areas on the fireball surface and other factors are investigated. Our analysis shows that the formula for the count rate of fireballs can be expressed as a function of , which is the observation timescale relative to the dynamical timescale of the fireball defined by R c /c, where R c is the fireball radius measured at an associated local time. The profile of light curves of fireballs depends only on the relative timescale, entirely independent of the real timescale and the real size of the objects. It displays in detail how a cutoff tail or a turnover feature (called the cutoff tail problem) in the decay phase of a light curve can arise. This feature is a consequence of a hot spot on the fireball surface, moving toward the observer, and has been observed in a few cases previously. Local pulses suddenly dimming produce light curves bearing a certain decay form (called a standard decay form) and exhibiting a sharp feature at their peaks. Light curves arising from gradually dimming local pulses are smooth at their peaks, and their profiles in the decay phase will obviously deviate from the standard form when the width of the local pulse is large enough. It is observed that light curves arising from relatively short local pulses should be the same, entirely independent of the local pulse shape. The impact of the rest-frame radiation form and of the variance of the form on the profile of light curves is insignificant, while the impact on the magnitude of the light curves is obvious. By performing fits to the count-rate light curves of six sample sources, we show how to obtain some physical parameters from the observed profile of the count rate of GRBs and show that there do exist some GRBs for which the profiles of their count rate light curves can be described by the formula provided. In addition, the analysis reveals that the Doppler effect of fireballs could lead to a power-law relationship between the FWHM of pulses and energy, which has been observed previously by many authors.

Secondary organic aerosol (SOA) produced by atmospheric oxidation of primary emitted precursors is a major contributor to fine particulate matter (PM2.5) air pollution worldwide. Observations during winter haze pollution episodes in urban China show that most of this SOA originates from fossil-fuel combustion but the chemical mechanisms involved are unclear. Here we report field observations in a Beijing winter haze event that reveal fast aqueous-phase conversion of fossil-fuel primary organic aerosol (POA) to SOA at high relative humidity. Analyses of aerosol mass spectra and elemental ratios indicate that ring-breaking oxidation of POA aromatic species, leading to functionalization as carbonyls and carboxylic acids, may serve as the dominant mechanism for this SOA formation. A POA origin for SOA could explain why SOA has been decreasing over the 2013–2018 period in response to POA emission controls even as emissions of volatile organic compounds (VOCs) have remained flat.

Starting from XMM-Newton EPIC pn data, we present the X-ray variability characteristics of PKS 2155À304 using a simple analysis of the excess variance, 2 XS , and of the fractional rms variability amplitude, F var . The scatter in 2 XS and F var , calculated using 500 s long segments of the light curves, is smaller than the scatter expected for red-noise variability. This alone does not imply that the underlying process responsible for the variability of the source is stationary, since the real changes of the individual variance estimates are possibly smaller than the large scatters expected for a red-noise process. In fact, the averaged 2 XS and F var , which reduce the fluctuations of the individual variances, also change with time, indicating nonstationary variability. Moreover, both the averaged XS (absolute rms variability amplitude) and the averaged F var show linear correlation with source flux, but in an opposite sense: XS correlates with flux, but F var anticorrelates with flux. These correlations suggest that the variability process of the source is strongly nonstationary, as random scatters of variances should not yield any correlation. Spectra of F var were constructed to compare variability amplitudes in different energy bands. We found that the fractional rms variability amplitude of the source, when significant variability is observed, increases logarithmically with the photon energy, indicating significant spectral variability. The point-to-point variability amplitude may also track this trend, suggesting that the slopes of the power spectral density of the source are energy-independent. Using the normalized excess variance, the black hole mass of PKS 2155À304 was estimated to be about 1:45 ; 10 8 M . This is compared and contrasted with the estimates derived from measurements of the host galaxies.

We present a detailed analysis on the spectral lags of the short gamma‐ray bursts (GRBs) and compare them with that of the long GRBs by using the CGRO (Compton Gamma‐ray Observatory)/BATSE GRB catalogue. Our sample includes 308 short GRBs and 1008 long GRBs. The light curves of these GRBs are in 64‐ms time bin and they have at least one long and intense pulse, which satisfies δT≥ 0.512 s at c= 1σ and cmax≥ 6σ, where δT is the pulse duration, c is the photon counts and σ is the standard error of the background. We calculate the cross‐correlation function (CCF) for the light curves in 25–55 and 110–300 keV bands and derive the spectral lag by fitting the CCF with the Gaussian model. Our results are as follows. (i) The spectral lag distribution of the short GRBs is significantly different from that of the long GRBs. Excluding the statistical fluctuation effect, a proportion of ∼17 per cent of the short GRBs has a negative spectral lag, i.e. the hard photons are being lag behind the soft photons. We do not find any peculiar features from their light curves to distinguish these bursts from those with a positive spectral lag. We argue that a more physical mechanism dominated the hard lag may be hid behind the morphological features of the light curves. This should be a great challenge to the current GRB models. We note that this proportion is consistent with the proportion of short GRBs correlated with nearby galaxies newly discovered by Tanvir et al., although it is unclear if these short GRBs are indeed associated with the sources originated at low redshift. (ii) While the spectral lags of the long GRBs are strongly correlated with the pulse durations, they are not for the short GRBs. However, the ratios of the spectral lag to the pulse duration for the short and long GRBs are normal distributions at 0.023 and 0.046, respectively, with the sample width, indicating that the curvature effect alone could not explain the difference of the spectral lags between the two types of GRBs. The hydrodynamic time‐scales of the outflows and the radiative processes at work in GRBs might also play an important role as suggested by Daigne and Mochkovitch.