[i] An inventory of air pollutant emissions in Asia in the year 2000 is developed to support atmospheric modeling and analysis of observations taken during the TRACE-P experiment funded by the National Aeronautics and Space Administration (NASA) and the ACE-Asia experiment funded by the National Science Foundation (NSF) and the National Oceanic and Atmospheric Administration (NOAA). Emissions are estimated for all major anthropogenic sources, including biomass burning, in 64 regions of Asia. We estimate total Asian emissions as follows: 34.3 Tg SO 2 , 26.8 Tg NO x , 9870 Tg CO 2 , 279 Tg CO, 107 Tg CH 4 , 52.2 Tg NMVOC, 2.54 Tg black carbon (BC), 10.4 Tg organic carbon (OC), and 27.5 Tg NH 3 . In addition, NMVOC are speciated into 19 subcategories according to functional groups and reactivity. Thus we are able to identify the major source regions and types for many of the significant gaseous and particle emissions that influence pollutant concentrations in the vicinity of the TRACE-P and ACE-Asia field measurements. Emissions in China dominate the signature of pollutant concentrations in this region, so special emphasis has been placed on the development of emission estimates for China. China's emissions are determined to be as follows: 20.4 Tg SO 2 , 11.4 Tg NO x , 3820 Tg CO 2 , 116 Tg CO, 38.4 Tg CH 4 , 17.4 Tg NMVOC, 1.05 Tg BC, 3.4 Tg OC, and 13.6 Tg NH 3 . Emissions are gridded at a variety of spatial resolutions from 1° × 1° to 30 s x 30 s, using the exact locations of large point sources and surrogate GIS distributions of urban and rural population, road networks, landcover, ship lanes, etc. The gridded emission estimates have been used as inputs to atmospheric simulation models and have proven to be generally robust in comparison with field observations, though there is reason to think that emissions of CO and possibly BC may be underestimated. Monthly emission estimates for China are developed for each species to aid TRACE-P and ACE-Asia data interpretation. During the observation period of March/ April, emissions are roughly at their average values (one twelfth of annual). Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of ±16% for SO 2 to a high of ±450% for OC.

An inventory of gaseous and primary aerosol emissions in Asia in the year 2000 : NASA global tropospheric experiment transport and chemical evolution over the pacific (TRACE-P): Measurements and analysis (TRACEP1)

. To tackle the problem of severe air pollution, China has implemented active
clean air policies in recent years. As a consequence, the emissions of major
air pollutants have decreased and the air quality has substantially improved.
Here, we quantified China's anthropogenic emission trends from 2010 to 2017
and identified the major driving forces of these trends by using a
combination of bottom-up emission inventory and index decomposition analysis
(IDA) approaches. The relative change rates of China's anthropogenic
emissions during 2010–2017 are estimated as follows: −62  % for
 SO2 , −17  % for NOx , +11  % for nonmethane
volatile organic compounds (NMVOCs), +1  % for NH3 , −27  %
for CO, −38  % for PM 10 , −35  % for PM 2.5 , −27  %
for BC, −35  % for OC, and +16  % for CO2 . The IDA results
suggest that emission control measures are the main drivers of this
reduction, in which the pollution controls on power plants and industries are
the most effective mitigation measures. The emission reduction rates markedly
accelerated after the year 2013, confirming the effectiveness of China's
Clean Air Action that was implemented since 2013. We estimated that during
2013–2017, China's anthropogenic emissions decreased by 59 % for
 SO2 , 21 % for NOx , 23 % for CO, 36 % for
PM 10 , 33 % for PM 2.5 , 28 % for BC, and 32 % for OC.
NMVOC emissions increased and NH3 emissions remained stable during
2010–2017, representing the absence of effective mitigation measures for
NMVOCs and NH3 in current policies. The relative contributions of
different sectors to emissions have significantly changed after several
years' implementation of clean air policies, indicating that it is paramount
to introduce new policies to enable further emission reductions in the
future.

https://acp.copernicus.org/articles/18/14095/2018/acp-18-14095-2018.pdf

Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions

From 2013 to 2017, with the implementation of the toughest-ever clean air policy in China, significant declines in fine particle (PM2.5) concentrations occurred nationwide. Here we estimate the drivers of the improved PM2.5 air quality and the associated health benefits in China from 2013 to 2017 based on a measure-specific integrated evaluation approach, which combines a bottom-up emission inventory, a chemical transport model, and epidemiological exposure-response functions. The estimated national population-weighted annual mean PM2.5 concentrations decreased from 61.8 (95%CI: 53.3-70.0) to 42.0 µg/m3 (95% CI: 35.7-48.6) in 5 y, with dominant contributions from anthropogenic emission abatements. Although interannual meteorological variations could significantly alter PM2.5 concentrations, the corresponding effects on the 5-y trends were relatively small. The measure-by-measure evaluation indicated that strengthening industrial emission standards (power plants and emission-intensive industrial sectors), upgrades on industrial boilers, phasing out outdated industrial capacities, and promoting clean fuels in the residential sector were major effective measures in reducing PM2.5 pollution and health burdens. These measures were estimated to contribute to 6.6- (95% CI: 5.9-7.1), 4.4- (95% CI: 3.8-4.9), 2.8- (95% CI: 2.5-3.0), and 2.2- (95% CI: 2.0-2.5) µg/m3 declines in the national PM2.5 concentration in 2017, respectively, and further reduced PM2.5-attributable excess deaths by 0.37 million (95% CI: 0.35-0.39), or 92% of the total avoided deaths. Our study confirms the effectiveness of China's recent clean air actions, and the measure-by-measure evaluation provides insights into future clean air policy making in China and in other developing and polluting countries.

https://www.pnas.org/content/pnas/116/49/24463.full.pdf

Drivers of improved PM2.5 air quality in China from 2013 to 2017.

. In 2013, China's government published the Air Pollution Prevention and
Control Action Plan (APPCAP) with a specific target for Beijing, which aims
to reduce annual mean PM 2.5 concentrations in Beijing to
60  µ g m −3 in 2017. During 2013–2017, the air quality in
Beijing was significantly improved following the implementation of various
emission control measures locally and regionally, with the annual mean
PM 2.5 concentration decreasing from 89.5  µ g m −3 in 2013
to 58  µ g m −3 in 2017. As meteorological conditions were more
favourable to the reduction of air pollution in 2017 than in 2013 and 2016,
the real effectiveness of emission control measures on the improvement of air
quality in Beijing has frequently been questioned. In this work, by combining a detailed bottom-up emission inventory over
Beijing, the MEIC regional emission inventory and the WRF-CMAQ (Weather
Research and Forecasting Model and Community Multiscale Air Quality) model, we
attribute the improvement in Beijing's PM 2.5 air quality in 2017
(compared to 2013 and 2016) to the following factors: changes in
meteorological conditions, reduction of emissions from surrounding regions,
and seven specific categories of local emission control measures in Beijing.
We collect and summarize data related to 32 detailed control measures
implemented during 2013–2017, quantify the emission reductions associated
with each measure using the bottom-up local emission inventory in 2013,
aggregate the measures into seven categories, and conduct a series of CMAQ
simulations to quantify the contribution of different factors to the
PM 2.5 changes. We found that, although changes in meteorological conditions partly explain
the improved PM 2.5 air quality in Beijing in 2017 compared to 2013 (3.8  µ g m −3 ,
12.1 % of total), the rapid decrease in
PM 2.5 concentrations in Beijing during 2013–2017 was dominated by local
(20.6  µ g m −3 , 65.4 %) and regional (7.1  µ g m −3 ,
22.5 %) emission reductions. The seven categories of
emission control measures, i.e. coal-fired boiler control,
clean fuels in the residential sector, optimize industrial structure,
fugitive dust control, vehicle emission control,
improved end-of-pipe control, and integrated treatment of VOCs,
reduced the PM 2.5 
concentrations in Beijing by 5.9, 5.3, 3.2, 2.3, 1.9, 1.8, and 0.2  µ g m −3 ,
respectively, during 2013–2017. We also found
that changes in meteorological conditions could explain roughly 30 % of
total reduction in PM 2.5 concentration during 2016–2017 with more
prominent contribution in winter months (November and December). If the
meteorological conditions in 2017 had remained the same as those in 2016,
the annual mean PM 2.5 concentrations would have increased from
58 to 63  µ g m −3 , exceeding the
target established in the APPCAP. Despite the remarkable impacts from
meteorological condition changes, local and regional emission reductions
still played major roles in the PM 2.5 decrease in Beijing during
2016–2017, and clean fuels in the residential sector, coal-fired boiler control,
and optimize industrial structure were the three most effective local measures
(contributing reductions of 2.1, 1.9, and 1.5  µ g m −3 ,
respectively). Our study confirms the effectiveness of clean air actions in
Beijing and its surrounding regions and reveals that a new generation of
control measures and strengthened regional joint emission control measures
should be implemented for continued air quality improvement in Beijing
because the major emitting sources have changed since the implementation of
the clean air actions.

/pdf/dominant-role-of-emission-reduction-in-pm-2-5-air-quality-54bhk8fngf.pdf

Dominant role of emission reduction in PM 2.5 air quality improvement in Beijing during 2013–2017: a model-based decomposition analysis

. The COVID-19 pandemic lockdowns led to a sharp drop in
socio-economic activities in China in 2020, including reductions in fossil
fuel use, industry productions, and traffic volumes. The short-term impacts
of lockdowns on China's air quality have been measured and reported,
however, the changes in anthropogenic emissions have not yet been assessed
quantitatively, which hinders our understanding of the causes of the air
quality changes during COVID-19. Here, for the first time, we report the
anthropogenic air pollutant emissions from mainland China by using a
bottom-up approach based on the near-real-time data in 2020 and use the
estimated emissions to simulate air quality changes with a chemical
transport model. The COVID-19 lockdown was estimated to have reduced China's
anthropogenic emissions substantially between January and March in 2020,
with the largest reductions in February. Emissions of SO 2 , NO x ,
CO, non-methane volatile organic compounds (NMVOCs), and primary PM 2.5 were estimated to have decreased by
27 %, 36 %, 28 %, 31 %, and 24 %, respectively, in February 2020
compared to the same month in 2019. The reductions in anthropogenic
emissions were dominated by the industry sector for SO 2 and PM 2.5 
and were contributed to approximately equally by the industry and
transportation sectors for NO x , CO, and NMVOCs. With the spread of
coronavirus controlled, China's anthropogenic emissions rebounded in April
and since then returned to the comparable levels of 2019 in the second half
of 2020. The provinces in China have presented nearly synchronous decline
and rebound in anthropogenic emissions, while Hubei and the provinces
surrounding Beijing recovered more slowly due to the extension of lockdown
measures. The ambient air pollution presented much lower concentrations
during the first 3 months in 2020 than in 2019 while rapidly returning to comparable levels afterward, which have been reproduced by the air
quality model simulation driven by our estimated emissions. China's monthly
anthropogenic emissions in 2020 can be accessed from
 https://doi.org/10.6084/m9.figshare.c.5214920.v2 (Zheng et al., 2021) by
species, month, sector, and province.

/pdf/changes-in-china-s-anthropogenic-emissions-and-air-quality-b6mvnhg7br.pdf

Changes in China's anthropogenic emissions and air quality during the COVID-19 pandemic in 2020

A high-resolution air pollutants emission inventory in 2013 for the Beijing-Tianjin-Hebei region, China

Linking metrics of landscape pattern to hydrological process in a lotic wetland

Strong reciprocal interactions exist between landscape patterns and ecological processes. In wetlands, hydrology is the dominant abiotic driver of ecological processes and both controls, and is controlled, by vegetation presence and patterning. We focus on binary patterning in the Everglades ridge-slough landscape, where longitudinally connected flow, principally in sloughs, is integral to landscape function. Patterning controls discharge competence in this low-gradient peatland, with important feedbacks on hydroperiod and thus peat accretion and patch transitions. To quantitatively predict pattern effects on hydrologic connectivity and thus hydroperiod. We evaluated three pattern metrics that vary in their hydrologic specificity. (1) Landscape discharge competence considers elongation and patch-type density that capture geostatistical landscape features. (2) Directional connectivity index (DCI) extracts both flow path and direction based on graph theory. (3) Least flow cost (LFC) is based on a global spatial distance algorithm strongly analogous to landscape water routing, where ridges have higher flow cost than sloughs because of their elevation and vegetation structure. Metrics were evaluated in comparison to hydroperiod estimated using a numerically intensive hydrologic model for synthetic landscapes. Fitted relationships between metrics and hydroperiod for synthetic landscapes were extrapolated to contemporary and historical maps to explore hydroperiod trends in space and time. Both LFC and DCI were excellent predictors of hydroperiod and useful for diagnosing how the modern landscape has reorganized in response to modified hydrology. Metric simplicity and performance indicates potential to provide hydrologically explicit, computationally simple, and spatially independent predictions of landscape hydrology, and thus effectively measure of restoration performance.

http://www.watershedecology.org/uploads/1/2/7/3/12731039/yuan_et_al._2015.pdf

Aquaculture has been responsible for an impressive growth in the global supply of seafood. As of 2016, more than half of all global seafood production comes from aquaculture. To meet future global seafood demands, there is need and opportunity to expand marine aquaculture production in ways that are both socially and ecologically sustainable. This requires integrating biophysical, social, and engineering sciences. Such interdisciplinary research is difficult due to the complexity and multi-scale aspects of marine aquaculture and inherent challenges researchers face working across disciplines. To this end, we developed a framework based on Elinor Ostrom’s social–ecological system framework (SESF) to guide interdisciplinary research on marine aquaculture. We first present the framework and the social–ecological system variables relevant to research on marine aquaculture and then illustrate one application of this framework to interdisciplinary research underway in Maine, the largest producer of marine aquaculture products in the United States. We use the framework to compare oyster aquaculture in two study regions, with a focus on factors influencing the social and biophysical carrying capacity. We conclude that the flexibility provided by the SESF is well suited to inform interdisciplinary research on marine aquaculture, especially comparative, cross-case analysis.

https://www.mdpi.com/2071-1050/11/9/2522/pdf

A social-ecological system framework for marine aquaculture research.

Aquaculture site selection, like most site suitability analyses, requires the assembly and combination of multiple variables. Geographic information systems GIS and multi-criteria evaluation (MCE) based approaches are commonly used for aquaculture site selection and demonstrate the integration of various information sources relevant for siting aquaculture. These analyses, however, tend to be one-time and result in a fixed site suitability plan. Within a dynamic marine environment experiencing potential regime shifts, a siting support tool that integrates new and evolving spatio-temporal data has benefits. This paper presents a flexible Voronoi cell-based GIS model for marine aquaculture siting. Rather than a one-time specification of suitable locations, the approach uses similarity measures on the characteristics of Voronoi cells to find cells with similar characteristics. We calculate a weighted aquaculture site tenure value for Voronoi cells that have been or are occupied by aquaculture farm sites. High scoring cells suggest suitable sites and serve as targets for similarity queries. We apply the approach to a case study on the coast of Maine using an R Shiny application to demonstrate the use of the framework for finding sites with similar characteristics.

https://www.mdpi.com/2077-1312/8/2/96/pdf

Jing Yuan

Papers

A high-resolution air pollutants emission inventory in 2013 for the Beijing-Tianjin-Hebei region, China

Linking metrics of landscape pattern to hydrological process in a lotic wetland

Linking metrics of landscape pattern to hydrological process in a lotic wetland

A social-ecological system framework for marine aquaculture research.

A Method for Heterogeneous Spatio-Temporal Data Integration in Support of Marine Aquaculture Site Selection