Insight into the Role of NH₃/NH₄⁺ and NO_<i>x</i>/NO₃^– in the Formation of Nitrogen-Containing Brown Carbon in Chinese Megacities

Particulate brown carbon (BrC) plays a crucial role in the global radiative balance due to its ability to absorb light. However, the effect of molecular formation on the light absorption properties of BrC remains poorly understood. In this study, atmospheric BrC samples collected from six Chinese megacities in winter and summer were characterized through ultrahigh-performance liquid chromatography coupled with Orbitrap mass spectrometry (UHPLC-Orbitrap MS) and light absorption measurements. The average values of BrC light absorption coefficient at a wavelength of 365 nm (babs365) in winter were approximately 4.0 times higher than those in summer. Nitrogen-containing organic molecules (CHNO) were identified as critical components of light-absorbing substances in both seasons, underscoring the importance of N-addition in BrC. These nitrogen-containing BrC chromophores were more closely related to nitro-containing compounds originating from biomass burning and nitrogen oxides (NOx)/nitrate (NO3–) reactions in winter. In summer, they were related to reduced N-containing compounds formed in ammonia (NH3)/ammonium (NH4+) reactions. The NH3/NH4+-mediated reactions contributed more to secondary BrC in summer than winter, particularly in southern cities. Compared with winter, the higher O/Cw, lower molecule conjugation indicator (double bond equivalent, DBE), and reduced BrC babs365 in summer suggest a possible bleaching mechanism during the oxidation process. These findings strengthen the connection between molecular composition and the light-absorbing properties of BrC, providing insights into the formation mechanisms of BrC chromophores across northern and southern Chinese cities in different seasons

Long-Term Trends in Visibility and at Chengdu, China

<div><p>Long-term (1973 to 2010) trends in visibility at Chengdu, China were investigated using meteorological data from the U.S. National Climatic Data Center. The visual range exhibited a declining trend before 1982, a slight increase between 1983 and 1995, a sharp decrease between 1996 and 2005, and some improvements after 2006. The trends in visibility were generally consistent with the economic development and implementation of pollution controls in China. Intensive PM_2.5 measurements were conducted from 2009 to 2010 to determine the causes of visibility degradation. An analysis based on a modification of the IMPROVE approach indicated that PM_2.5 ammonium bisulfate contributed 27.7% to the light extinction coefficient (<i>b_ext</i>); this was followed by organic mass (21.7%), moisture (20.6%), and ammonium nitrate (16.3%). Contributions from elemental carbon (9.4%) and soil dust (4.3%) were relatively minor. Anthropogenic aerosol components (sulfate, nitrate, and elemental carbon) and moisture at the surface also were important determinants of the aerosol optical depth (AOD) at 550 nm, and the spatial distributions of both <i>b_ext</i> and AOD were strongly affected by regional topography. A Positive Matrix Factorization receptor model suggested that coal combustion was the largest contributor to PM_2.5 mass (42.3%) and the dry-air light-scattering coefficient (47.7%); this was followed by vehicular emissions (23.4% and 20.5%, respectively), industrial emissions (14.9% and 18.8%), biomass burning (12.8% and 11.9%), and fugitive dust (6.6% and 1.1%). Our observations provide a scientific basis for improving visibility in this area.</p></div

Daily variations of the contributions of PM_2.5 chemical components and aerosol moisture to the light extinction coefficient (<i>b_ext</i>) for the intensive sampling period based on the revised IMPROVE equation.

<p>The aerosol moisture contributions were calculated from <i>b_ext</i> under ambient condition subtracts <i>b_ext</i> under dry condition.</p

Annual variations of ridit values during 1973–2010 in Chengdu.

<p>Ridit values >0.5 mean that the visual range for the year was better than the reference distribution established from the 1973–2010 data; the opposite is true for value <0.5. Solid lines are linear fits of the ridit trends.</p

Left panel: Spatial and seasonal distributions of average MODIS/Aqua AOD at 550 nm.

<p>Right panel: light extinction coefficient (<i>b_ext</i>) estimated from Koschmieder’s formula over the Sichuan Basin during March 2009 to February 2010.</p

Coefficients of the regression model for visual range (VR) and air pollution index (API) during Period-1 (1973–1982), Period-2 (1983–1995), Period-3 (1996–2005), and Period-4 (2006–2010) in Chengdu.

a<p>Standard deviation.</p>b<p>Sample number of the monthly average values of each VR and API.</p>c<p>Correlation coefficient.</p>d<p>Annual rate of change: R = 12<i>β</i> (km yr⁻¹ for VR for API yr⁻¹).</p>e<p>Period-3 for the API was from 2000–2005.</p

Average source contribution (in percent) for each PMF source factor to PM_2.5 mass concentration and dry particle light scattering coefficient (<i>b_sp,dry</i>).

<p>Average source contribution (in percent) for each PMF source factor to PM_2.5 mass concentration and dry particle light scattering coefficient (<i>b_sp,dry</i>).</p

Average chemical component concentrations and meteorological parameters for the best and worst visual ranges (VRs).

a<p>Units: PM_2.5 and chemical species, µg m⁻³; Relative humidity (RH), %; Wind speed (WS), m s⁻¹; Mixed layer depth (MLD), m.</p>b<p>daily average VR values for the 2.5% least impaired days.</p>c<p>daily average VR values for the 2.5% most impaired days.</p>d<p>S.D.: Standard deviation.</p>e<p>OM: Organic mass = 1.8×OC.</p>f<p>EC: Elemental carbon.</p

Scatter plots of ammonium calculated from (a) 0.29×[NO₃⁻] +0.19×[SO₄²⁻] and (b) 0.29×[NO₃⁻] +0.38×[SO₄²⁻] versus ammonium measured by ion chromatography.

<p>Scatter plots of ammonium calculated from (a) 0.29×[NO₃⁻] +0.19×[SO₄²⁻] and (b) 0.29×[NO₃⁻] +0.38×[SO₄²⁻] versus ammonium measured by ion chromatography.</p