Philippa Kaur

NEWS
10 September 2020

Fires are releasing record levels of carbon dioxide, partly because they are burning ancient peatlands that have been a carbon sink.

Alexandra Witze

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Northern fires (like the one shown here in the Novosibirsk Region of south Siberia) released record-setting amounts of carbon dioxide this year.Credit: Kirill Kukhmar/TASS/Getty

Wildfires blazed along the Arctic Circle this summer, incinerating tundra, blanketing Siberian cities in smoke and capping the second extraordinary fire season in a row. By the time the fire season waned at the end of last month, the blazes had emitted a record 244 megatonnes of carbon dioxide — that’s 35% more than last year, which also set records. One culprit, scientists say, could be peatlands that are burning as the top of the world melts.
Peatlands are carbon-rich soils that accumulate as waterlogged plants slowly decay, sometimes over thousands of years. They are the most carbon-dense ecosystems on Earth; a typical northern peatland packs in roughly ten times as much carbon as a boreal forest. When peat burns, it releases its ancient carbon to the atmosphere, adding to the heat-trapping gases that cause climate change.

Nearly half the world’s peatland-stored carbon lies between 60 and 70 degrees north, along the Arctic Circle. The problem with this is that historically frozen carbon-rich soils are expected to thaw as the planet warms, making them even more vulnerable to wildfires and more likely to release large amounts of carbon. It’s a feedback loop: as peatlands release more carbon, global warming increases, which thaws more peat and causes more wildfires. A study published last month1 shows that northern peatlands could eventually shift from being a net sink for carbon to a net source of carbon, further accelerating climate change.
The unprecedented Arctic wildfires of 2019 and 2020 show that transformational shifts are already under way, says Thomas Smith, an environmental geographer at the London School of Economics and Political Science. “Alarming is the right term.”
Zombie fires
The fire season in the Arctic kicked off unusually early this year: as early as May, there were fires blazing north of the tree line in Siberia, which normally wouldn’t happen until around July. One reason is that temperatures in winter and spring were warmer than usual, priming the landscape to burn. It’s also possible that peat fires had been smouldering beneath the ice and snow all winter and then emerged, zombie-like, in the spring as the snow melted. Scientists have shown that this kind of low-temperature, flameless combustion can burn in peat and other organic matter, such as coal, for months or even years.
Because of the early start, individual Arctic wildfires have been burning for longer than usual, and “they’re starting much farther north than they used to — in landscapes that we thought were fire-resistant rather than fire-prone”, says Jessica McCarty, a geographer at Miami University in Oxford, Ohio.

Sources: Copernicus Atmosphere Monitoring Service/European Centre for Medium-Range Weather Forecasts; Hugelius, G. et al. Proc. Natl. Acad. Sci. USA 117, 20438–20446 (2020)

Researchers are now assessing just how bad this Arctic fire season was. The Russian Wildfires Remote Monitoring System catalogued 18,591 separate fires in Russia’s two easternmost districts, with a total of nearly 14 million hectares burnt, says Evgeny Shvetsov, a fire specialist at the Sukachev Institute of Forest, which is part of the Russian Academy of Sciences in Krasnoyarsk. Most of the burning happened in permafrost zones, where the ground is normally frozen year-round.
To estimate the record carbon dioxide emissions, scientists with the European Commission’s Copernicus Atmosphere Monitoring Service used satellites to study the wildfires’ locations and intensity, and then calculated how much fuel each had probably burnt. Yet even that is likely to be an underestimate, says Mark Parrington, an atmospheric scientist at the European Centre for Medium-Range Weather Forecasts in Reading, UK, who was involved in the analysis. Fires that burn in peatland can be too low-intensity for satellite sensors to capture.
The problem with peat
How much this year’s Arctic fires will affect global climate over the long term depends on what they burnt. That’s because peatlands, unlike boreal forest, do not regrow quickly after a fire, so the carbon released is permanently lost to the atmosphere.
Smith has calculated that about half of the Arctic wildfires in May and June were on peatlands — and that in many cases, the fires went on for days, suggesting that they were fuelled by thick layers of peat or other soil rich in organic matter.

And the August study1 found that there are nearly four million square kilometres of peatlands in northern latitudes. More of that than previously thought is frozen and shallow — and therefore vulnerable to thawing and drying out, says Gustaf Hugelius, a permafrost scientist at Stockholm University who led the investigation. He and his colleagues also found that although peatlands have been helping to cool the climate for thousands of years, by storing carbon as they accumulate, they will probably become a net source of carbon being released into the atmosphere — which could happen by the end of the century.
Fire risk in Siberia is predicted to increase as the climate warms2, but by many measures, the shift has already arrived, says Amber Soja, an environmental scientist who studies Arctic fires at the US National Institute of Aerospace in Hampton, Virginia. “What you would expect is already happening,” she says. “And in some cases faster than we would have expected.”

doi: 10.1038/d41586-020-02568-y

References

1.
Hugelius, G. et al. Proc. Natl Acad. Sci. USA 117, 20438–20446 (2020).

2.
Sherstyukov, B. G. & Sherstyukov, A. B. Russian Meteorol. Hydrol. 39, 292–301 (2014).

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Field investigation
This study was performed between from May 2017 and October 2019 at Xinsheng Aquaculture Professional Cooperative (121° 0′ 56″ N, 30° 58′ 17″ E) in Qingpu District, Shanghai, Eastern China. This region has a subtropical monsoon climate with a mean monthly air temperature of 17.6 ± 2.3 °C and mean monthly precipitation of 126.9 ± 24.6 mm.
Each RC paddy (667 m2) had a rice-growing area (80% of the total area), aquaculture area (10%) and ridge area (10%; Fig. 4A). In the aquaculture area, a 1.2 m deep ditch was dug to provide a more comfortable habitat for the crayfish and eels. The ridge had a height of 40 cm, and it was covered with a high-density polyethylene film to prevent the aquatic animals from escaping. Every May, rice (Oryza sativa L., Qing-Xiang-Ruan-Geng) seedlings were transplanted from a nursery into the paddies at a planting density of 20 × 20 cm (one seedling on each hill). Moreover, the juvenile crayfish weighing 1.5 ± 0.3 g were released into the paddies according to the standard of 45,000 juveniles per hectare, and the crayfish were allowed to self-propagate inside the rice paddies. A total of nine RC paddies were divided into three groups according to the rearing density of the eels: control group (C), low-density group (LD) and high-density group (HD) with rearing densities of 0, 6000 and 12,000 ind. ha−1, respectively. The LD and HD groups were supplemented with juvenile eels at a density of 2000 and 4000 ind. ha−1 in June 2018 and 2019. The average weights of juvenile eels in 2017, 2018 and 2019 were 21.4 ± 1.8, 24.1 ± 0.9 and 26.8 ± 1.1 g, respectively. All juvenile crayfish and eels were purchased from Shanghai Xiangsheng Aquaculture Cooperative. In the aquaculture area, floating plants, such as duckweed (Lemna minor L.) and foxtail (Myriophyllum spicatum L.), covered one-third of the water surface. The soil contained 20.6–23.7 g kg−1 of organic matter, 0.7–1.2 g kg−1 of total N and 0.31–0.37 g kg−1 of total P.
Figure 4

Photographs of the paddies in the field investigation (A) and plots in the mesocosm experiment (B).

Full size image

Only basal fertilizer was used for rice cultivation, and it contained 587 kg ha−1 of urea (46.4% N), 625 kg ha−1 of superphosphate and 150 kg ha−1 of potassium chloride. Every day, 500 g of commercial fish diet (5.83% N) was applied, and no pesticides or herbicides were used in the paddies.
In late August, the mature crayfish and eels were collected using ground cages to measure the aquatic product yields. The immature crayfish and eels were returned to the paddy fields during the collection. After the rice was harvested, the rice grains were air-dried and weighed to estimate the rice yield. The N content of the rice grains and aquatic animals was determined using the semi-micro Kjeldahl method33. Before testing, rice grains, crayfish and eels were weighted, dried at 65 °C and ground. Then, all the samples were digested with concentrated sulphuric acid (H2SO4) and hydrogen peroxide.
Water samples were collected every month during the co-culture period. Three duplicate 500 mL water samples were collected from 0 to 10 cm below the surface in the aquaculture area; the three subsamples were combined to obtain one sample per paddy. In the laboratory, the total N content of the water was analysed using UV spectrophotometry after digestion by alkaline potassium persulfate oxidation.
Soil samples were collected after the rice-planting period. In each paddy, three samples were collected from a rice-planting area of 0.25 m × 0.25 m × 0.10 m. All the soil samples were air-dried, ground, passed through a 0.15 mm sieve and digested with K2SO4–CuSO4–Se solution. Then, the semi-micro Kjeldahl method was used to test the total N content of the soil.
The N2O flux rate was measured using the static chamber method34. The size of the chamber was 1.0 m × 1.0 m × 1.0 m. The N2O samples were collected every half month between 8:30 and 10:30 AM from June to October. In each paddy, four gas samples were collected using 40 mL vacuum tubes at 10 min intervals (0, 10, 20 and 30 min after chamber closure). All samples were analysed with gas chromatography (GC 2010; Shimadzu, Kyoto, Japan). The N2O flux rate was calculated using the following equation:

$$F = rho times h times left[ {{273}/left( {{273} + T} right)} right] times {text{d}}C{text{/d}}t$$
(1)

where F is the N2O flux rate (μg N m−2 h−1); ρ, density of N2O at the standard state (μg m−3); h, height of the chamber (m); T, average temperature in the chamber during gas collection and dC/dt, concentration variation rate of N2O.
The ammonia volatilization flux was measured with a continuous airflow enclosure method35. The NH3 flux was measured every half month from 09:00 to 11:00 AM during the rice-planting period. NH3 was absorbed using boric acid, and 0.01 M H2SO4 was used to titrate the solution to determine the rate of NH3 volatilization. The ammonia volatilization flux was calculated using the following equation:

$$F = 14 times V times C times A^{ – 1} times t^{ – 1}$$
(2)

where F denotes the ammonia volatilization flux (mg N m−2 h−1); V, volume of H2SO4 titrated (L); C, concentration of H2SO4 (mol L−1); A, area of the chamber base (m2) and t, continuous measurement time.
In this study, all the data were shown as mean ± standard error of the mean (SEM) values. One-way ANOVA and Tukey’s test (SPSS V.16.0) were used to compare the differences of the yields and total N content among the three groups and three investigated years.
Mesocosm experiment
Between May and October 2019, the mesocosm experiment was conducted at Shanghai Academy of Agricultural Sciences. Each mesocosm consisted of an experimental plot (1.2 m × 1.2 m × 0.6 m) covered with a high-density polyethylene film (Fig. 4B). In each experiment plot, 30 kg of soil from Xinsheng Aquaculture Professional Cooperative was used to construct a rice-planting platform and an aquaculture ditch (40 cm in depth). The platform area was about three-fourth of the cross-sectional area of the plot.
A total of six mesocosms were constructed: three experimental plots (RCE) and three control plots (RC). In each plot, the rice seedlings were planted in hills (one seedling per hill) within rows in May, with 20 cm between rows and 20 cm between hills in the same row for the experimental and control plots. The fertilizers used in each plot contained 84.5 g of urea (N content, 46.8%; 15N abundance, 10.15%), 90 g of superphosphate and 15 g of potassium chloride. The duckweed was planted in the aquaculture area, and it covered 30% of the aquaculture zone. Mudsnails (Cipangopaludina cathayensis, 500 g) were added to each plot. After a month, 12 crayfish were cultured in each simulated paddy, and two eels were reared in each experiment plot. The proportion of crayfish and eels was set according to that in the LD group of field investigation. The crayfish feed was supplied once every day, and the daily allowance was about 3% of the estimated crayfish weight in each mesocosm. The rice and aquatic products were harvested in October.
Rice, crayfish and eel samples were collected to measure the total N content and 15N abundance. The total N content of the soil and organism samples were measured using the semi-micro Kjeldahl method after digestion with concentrated H2SO4 and hydrogen peroxide. The 15N abundance was measured in all samples by using the MAT-271 isotope mass spectrometer (Finnigan MAT, California). The accumulation of N in rice, crayfish and eels from N fertilizer was calculated using the following equations:

$${text{Percentage of accumulated N from fertilizer NDFF}});( % ) = {text{A}}% ;{text{E of the organism sample/A}}% ;{text{E of the fertilizer sample}} times 100$$
(3)

$${text{Amount of accumulated N from fertilizer}} = {text{organism N accumulation amount}} times {text{NDFF}}$$
(4)

$${text{N use efficiency NUE}});(% ) = {text{amount of N accumulated by the organism accumulated from N fertilizer/total N content of the fertilizer}} times 100$$
(5)

where A% E is the difference between the 15N abundance of the samples or 15N-labelled fertilizers and natural abundance of 15N.
The independent-samples t-test was used to determine the differences in total N, N use efficiency and percentage of N derived from fertilizer between RCE and RC at 95% confidence level by using SPSS 16.0 (P value  More

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In-flight dosing system
The system used in this work is detailed in Fig. 1. It can be broken down into three primary modules: (1) a “coarse tracking” system that uses a pair of stereoscopic cameras to identify the three dimensional location of a target subject, which is passed on to (2) the “fine tracking” system that uses a single higher speed camera and a fast scanning mirror (FSM) to keep the target in the middle of the field of view (FOV) of the camera using a proportional-integral-derivative (PID) control loop, and (3) the laser dosing system that fires the laser pulse, which is co-aligned with the fine tracking system to ensure the laser pulse is accurately applied to the subject even while it is moving. For both the coarse and fine tracking systems, subjects are identified by the size of their silhouettes generated from near infrared LED back-illumination or reflection. As demonstrated in Fig. 1b, the subject cages were 20 cm cubes constructed from clear acrylic, but with the side facing the tracking and dosing systems made of borosilicate glass, placed two meters away from the FSM. A high-speed video camera (Vision Research Phantom) was set up to record a small subset of experiments at 2000 frames per second as well. For all experiments, each cage contained only a single subject to be illuminated, or “dosed,” with the laser, and conditions were set such that a subject could be dosed only when it was at least 2.5 to 5 cm away from any wall of the cage to ensure the subject was flying normally, rather than taking wing or alighting.
Figure 1

Schematics and images of in-flight dosing setup. (a) Schematic (not to scale) of primary dosing system components and communication lines among them. Note that the mirror control and 3D position subsystems were run on separate kernels within the same PC. (b) Image of complete system setup including the test cage location, LED backlighting, and control electronics. Cameras for coarse and fine tracking, the fast scanning mirror (FSM), and dosing laser path are contained in the white circle and better seen in (c) close-up image of core tracking and dosing system components and alignment among them. Red line represents fine tracking image path, green line the dosing laser path (laser not shown), and yellow line the combined fine tracking and dosing laser path.

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Prior to commencing the laser dosing experiments, a number of parameters were defined to analyze the results, as detailed in Table 1 and Fig. 2. The key outcome, in line with our previous study15 and with WHO guidelines on insecticide treatments17, was whether the subjects were alive or disabled (i.e. dead or moribund) 24 h after the treatment. To characterize how well the subjects were tracked during the laser pulse, the tracking error was defined as how far the insect’s centroid was from the center of the fine tracking camera’s FOV. Other parameters defined in Table 1 relate to the “occlusion factor,” or how much of the laser beam’s energy (assuming a Gaussian profile) overlapped with the target’s outline, as demonstrated in Fig. 2. From the coarse tracking system’s output of xyz position of the target, we could also define velocity, speed, and both linear and angular acceleration of the subjects before, during, and after the laser pulse.
Table 1 Definitions of all parameters tracked or calculated for each in-flight dosing event.
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Figure 2

Representative fine tracking camera images of A. stephensi silhouettes. In each frame, the approximate outline of the thorax and abdomen is drawn (thick black lines) according to a set pixel intensity threshold. The centroid of this region is then calculated (intersection of red crosshairs) and compared with the center of the camera’s field of view (green dot) to determine the current tracking error and provide input to the fine tracking PID loop controlling the direction of the scanning mirror. The green circle around the green dot represents the spot size of the laser (2.5 mm diameter for all images shown here); occlusion factor represents how much of this laser spot (assuming a Gaussian profile) overlaps with the body of the subject. The images in (a) demonstrate a typical time course for a single subject in 1 ms intervals, and those in (b) show representative depictions of tracking errors ranging from 1 to 5 pixels for various subjects.

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Initial results with 532 nm laser
Initial experiments were conducted with A. stephensi subjects and a 532 nm laser (Verdi, Coherent) using parameters for power (3 W), pulse duration (25 ms), and laser spot diameter (2.5 mm) that were defined in our previous work15 as an optimal combination of potential cost and mortality performance. As seen in Supplemental Video 1, the system could be quite effective in disabling a flying mosquito with these parameters. Of note from the video, along with flight tracking data for numerous trials (not shown), the 25 ms pulse duration appeared to be short enough that the mosquitoes did not perceptibly alter their flight pattern during the pulse to throw off the tracking algorithm. From Fig. 3a,b, though, the initial system did not have consistent enough tracking performance, such that survival of the subjects was almost entirely dictated by how well the fine tracking system kept the target near the center of its FOV. From this initial experiment on a sample of 80 subjects, we set limits for mean and maximum tracking error during the laser pulse as 2 and 3 pixels, respectively, where each pixel represents ~ 250 μm. Figure 3c,d demonstrates that the system was able to achieve and maintain this performance after modifying the PID loop parameters, and that there was no longer any association between tracking performance and mortality at these conditions. These criteria were evaluated and assured for all mortality data reported in this manuscript (i.e. a Kruskal–Wallis test indicated no significant differences in tracking errors among subjects that survived or were disabled by the laser pulses). Further, Supplemental Fig. S1 shows that flight behavior, in particular speed and linear acceleration, did correlate with tracking accuracy, but that the system was able to meet the noted tracking requirements even at the extremes of flight behavior that could be observed in this study given the restricted flight volumes (though the typical values seen here largely align with available data for other Anopheles mosquitoes18,19).
Figure 3

Influence of mean and maximum tracking errors on mortality outcomes. Initial mortality outcomes using LD90 conditions with a 532 nm laser from previous anesthetized work showed strong correlation with (a) mean and (b) maximum tracking errors over the 25 ms pulse duration. After setting and achieving new tracking error targets of less than 2 pixels mean and 3 pixels max (see Fig. 2b for depictions of overlap between the laser spot and the subject for various error magnitudes), identical experiments no longer showed any correlation with (c) mean or (d) maximum tracking errors. Moribund and dead outcomes were grouped in (c) and (d), labeled “Disabled,” and subsequent experiments since they both represent functional kills and are grouped as such in WHO guidelines for insecticide trials.

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Mortality results were analyzed as in Fig. 4. Each data point represents the mortality outcomes (i.e. percentage of targets that were dead or moribund at 24 h, assuming acceptable control mortality  2 × that of the other experiments at 445 or 532 nm. Note that this greater LD90 fluence value for the smaller spot still represented a lower pulse energy than required for the larger spots given the spot size differential.
Table 2 List of in-flight dosing experimental conditions and resulting LD90 fluence values for A. stephensi subjects.
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Figure 5

Dose–response curves for all experiments from Table 2, other than the two constant pulse energy tests. The x-axis is plotted on a log scale to allow clear visualization of the curves at both low and high fluence values. Individual data points and error bars not shown for the sake of clarity. The intersections of the dashed horizontal line at 90% mortality with the dose–response curves indicate the LD90 points, which are also presented in Table 2, along with pseudo R2 values for the logistic regression fits.

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Dosing with near infrared wavelengths
Two near infrared (NIR) laser sources were examined as well—a custom constructed 1,064 nm (1 μm) fiber laser with a maximum output of 30 W and a commercial 1,570 nm (1.5 μm) fiber laser (IPG Photonics) with a maximum deliverable output of 11 W. The right-hand side of Fig. 5 shows the dose–response curves for these laser sources in addition to those using visible wavelengths. All of the infrared experiments resulted in significantly higher LD90 fluence levels compared with the visible lasers, and given the log scale of Fig. 5, varying experimental conditions with the 1 μm source led to comparatively larger changes in LD90 compared with the visible sources. As with the blue diode, the small spot (1.5 mm) had the highest LD90 fluence, but unlike the visible lasers, the larger spot (4 mm) offered a lower LD90 fluence compared with the baseline 2.5 mm. For the 1.5 μm source, there was insufficient mortality at the maximum power available from this source with a 2.5 mm spot and 25 ms pulse duration, so Fig. 5 shows a curve using slightly longer pulse durations to make up the additional fluence required. From the available data, it appears that the LD90 fluence levels for the 1 μm and 1.5 μm sources are at least at reasonably comparable levels.
Effects of longer pulse durations with lower power
We then further explored the effects of increasing the pulse duration to determine whether this could relax the optical power required from the laser source, given that laser cost is correlated with output power. Figure 6a,b show the results for the 532 nm and 1,064 nm sources, respectively, with spot diameters of 2.5 mm and pulse duration set to 25, 50, or 100 ms. In all cases, the optical power was adjusted according to the pulse duration to supply a constant pulse energy, and therefore fluence at approximately the LD75 level determined from previous experiments, which was chosen to ensure a low likelihood of any data point being 100% mortality (i.e., a saturated signal). As evidenced from Fig. 6a and Table 3, at 532 nm there was a significant drop off in mortality at the longer pulse durations, although the effect is smaller going from 50 to 100 ms compared with the initial drop from 25 to 50 ms. Similar, but smaller magnitude effects were seen with the 1 μm source in Fig. 6b and Table 3. Supplemental Fig. S2 shows that the tracking accuracy for these experiments somewhat mirrors the mortality performance, with a notable decrement from 25 to 50 ms but no significant change from 50 to 100 ms.
Figure 6

Mortality results for experiments with constant pulse energy achieved by proportionally adjusting the power according to pulse duration (25, 50, and 100 ms). Dosing was performed with both the (a) 532 nm and (b) 1,064 nm lasers, both using a 2.5 mm spot size, and set to the approximate LD75 fluence value from the dose–response curves (solid curves with dashed line 95% confidence intervals of logistic regression fit) from the corresponding 25 ms experiments. Error bars on individual points represent 95% confidence intervals of exact binomial probabilities. Statistical differences among mortality at the various pulse durations summarized in Table 3. The slight offsets on the x axis in (a) stem from slight changes in the beam spot size as a function of power.

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Table 3 Results of χ2 tests for mortality equivalence among experiments with constant pulse energy but varied amounts of power and pulse duration (25, 50, and 100 ms).
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Comparing dosing performance across other insects
Using the same system and procedures, experiments were also run on SWD and ACP subjects as a way of cursorily exploring laser-insect interaction for species of interest besides A. stephensi. For SWD, a full dose–response curve was created for the 1 μm laser with typical exposure settings (2.5 mm spot diameter and 25 ms pulse duration). The LD90 fluence from this work was 8.6 J/cm2, compared with 12.9 J/cm2 for A. stephensi under the same conditions. Tracking performance on SWD subjects was comparable to that for A. stephensi, given that flight parameters such as speed and acceleration were comparable as well. With ACP subjects, inducing them to fly in the cages was very difficult. As such, we could not process a sufficient number of subjects to create a proper dose–response curve. Based on the limited data available, and as demonstrated in Supplemental Video 2, the system as set for the mosquito LD90 condition with the 1 μm laser, 2.5 mm spot diameter and 25 ms pulse duration was effective in tracking and disabling the ACP subjects, despite their very different flying behavior (greater speeds and accelerations, limited durations) relative to the other test species.
Longer range dosing system demonstration
As a final proof of principle test, a long-range version of the system from Fig. 1 was configured. This system worked at a distance of 30 m instead of 2 m, facilitated primarily by modifying the optics used for the coarse and fine tracking systems. Given the reduced flexibility of this system, it was only used with the 1 μm laser with a 2.5 mm spot diameter and 25 ms pulse duration. Rather than acquiring new dose–response curves, which was impractical given the setup’s location in a building ~ 30 km away from the insect-housing chamber, we verified the efficacy of the system using the LD90 conditions established by short-range dosing. Supplemental Video 3 shows this system dosing an A. stephensi subject from the same stock as used for short-range testing, and Supplemental Video 4 shows the same for a wild-sourced Culex pipiens mosquito obtained in a local pond. Note that the wild C. pipiens was substantially larger in size than the lab-reared A. stephensi. In all of the limited number of tests of this system for both species (10 A. stephensi and 5 C. pipiens), the mortality rate was 100%. More

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Total P and available P
Fertilizer application significantly (P  0.05). The highest increase in available P concentration in NPK + S treatment observed in the 60–100 cm soil depth, with the increase of 111% and 115% in 60–80 cm and 80–100 cm soil depths, respectively, over CK treatment.
Figure 2

Effect of long-term application of chemical fertilizer, organic manure, and straw on total phosphorus (P) and available P concentrations. * indicates significant difference at P  3.6%) was associated with OM treatment, especially at the 0–20 and 20–40 cm soil depths (Fig. 3). The PAC values under NPK, NPK + S and OM treatments increased by 7.6%, 4.5% and 11.5% in the 0–20 cm soil depth and 4.2%, 1.3%, and 5.8% in 20–40 cm soil depth, respectively as compared to the CK treatment. However, PAC value for soil depth below 40 cm showed the trend, NPK  More