在北京上一次遭遇史上最嚴(yán)重空氣污染之后沒(méi)幾天，IBM就開(kāi)始著手為城市打造更加強(qiáng)大的污染監(jiān)測(cè)和預(yù)報(bào)體系，此舉引發(fā)了廣泛關(guān)注。藍(lán)色巨人的中國(guó)研究實(shí)驗(yàn)室正聯(lián)手北京環(huán)境保護(hù)局合作研發(fā)一款新系統(tǒng)。該公司還將與同樣受到霧霾困擾的約翰內(nèi)斯堡和新德里進(jìn)行合作。

據(jù)IBM綠色地平線（Green Horizons）污染與可再生能源預(yù)報(bào)項(xiàng)目首席研究員亨德里克?哈曼介紹，北京現(xiàn)有的污染監(jiān)測(cè)手段成本太高，精確度也不足。偌大的城市只有幾十個(gè)監(jiān)測(cè)站，盡管它們的測(cè)量十分準(zhǔn)確，想要得出北京污染的詳細(xì)情況卻還不夠。

作為這些監(jiān)測(cè)站的補(bǔ)充，IBM和北京環(huán)境保護(hù)局打算建立由幾百甚至幾千個(gè)廉價(jià)、聯(lián)網(wǎng)、易于維護(hù)、鞋盒大小的感應(yīng)器組成的監(jiān)測(cè)網(wǎng)絡(luò)。

感應(yīng)器獲得的數(shù)據(jù)將通過(guò)整合天氣模式，甚至空氣化學(xué)反應(yīng)的計(jì)算機(jī)模型進(jìn)行分析，最終生成精確到1平方千米的72小時(shí)污染預(yù)報(bào)。

物聯(lián)網(wǎng)和大數(shù)據(jù)的這種結(jié)合，正是城市智能化的縮影。

哈曼表示：“顯而易見(jiàn)的是，如果不能測(cè)量，就無(wú)法治理。”

在交通堵塞和天氣的問(wèn)題上，這顯然是真理，不過(guò)這里還有個(gè)疑問(wèn)——城市“治理”污染究竟是什么意思？相較于擺脫污染，測(cè)量污染究竟有多重要？

表面上看，空氣質(zhì)量監(jiān)測(cè)似乎只是一種反應(yīng)式措施。當(dāng)?shù)氐牧钗镂廴疽驗(yàn)樘鞖庠颍刻於加兴煌踔翆?yán)重的污染比作臺(tái)風(fēng)：“我面對(duì)臺(tái)風(fēng)無(wú)能為力，但我可以保護(hù)市民的安全。”在北京，保護(hù)市民免受污染就意味著學(xué)校停課放假，大型建筑調(diào)整進(jìn)氣口，政府發(fā)布警告建議人們減少室外活動(dòng)。

當(dāng)然，污染與臺(tái)風(fēng)不完全是一回事——首先，污染大部分是由人類造成的，人類也有一定的能力控制它，甚至每天都能控制。目前，北京的空氣預(yù)警系統(tǒng)給予市政府很大的權(quán)力，比如停止建設(shè)，減少工廠作業(yè)（包括污染嚴(yán)重的火力發(fā)電站），限制車輛出行、爆竹燃放和燒烤活動(dòng)。

這些限制措施會(huì)帶來(lái)巨大的經(jīng)濟(jì)損失，哈曼博士表示，IBM研發(fā)的顆粒物污染預(yù)測(cè)會(huì)讓政府的措施更具針對(duì)性——例如，如果關(guān)閉城市里某些區(qū)域的火力發(fā)電站對(duì)于減輕霧霾具有最大成效，那么就只需關(guān)閉這些發(fā)電站。

即便是短期關(guān)閉火電站，聽(tīng)起來(lái)也像是渡過(guò)霧霾高峰期的臨時(shí)緩解措施，沒(méi)有改變根本問(wèn)題。不過(guò)，哈曼博士相信，通過(guò)基于情景的預(yù)報(bào)，以及公眾意識(shí)的提高，更好的數(shù)據(jù)從長(zhǎng)期上會(huì)改善污染狀況。

該系統(tǒng)強(qiáng)大的建模能力，可以就某些項(xiàng)目提供更為詳盡的成本和收益分析，例如升級(jí)公共交通工具的替代能源，加裝煙囪過(guò)濾器，提高能源效率等。哈曼表示：“看看洛杉磯等城市（這些城市抗擊霧霾問(wèn)題已有數(shù)十年），隨著對(duì)污染問(wèn)題的測(cè)量和了解，他們逐步推行各種不同的措施。”

哈曼認(rèn)為，更好的測(cè)量手段通過(guò)提供市民可以關(guān)注的污染基準(zhǔn)，還能起到自下而上的監(jiān)督作用。這能讓城市居民個(gè)人的咳嗽和喘氣變成一個(gè)群體關(guān)心的問(wèn)題，換句話說(shuō)，這些信息有助于將污染問(wèn)題放在政治層面上解決。

不過(guò)在北京，官員還沒(méi)有接受這個(gè)理念，公民時(shí)常難以獲取污染數(shù)據(jù)信息。盡管中國(guó)已經(jīng)有了采用其他能源的傾向，但依舊在很大程度上依賴火力發(fā)電。（財(cái)富中文網(wǎng)）

譯者：嚴(yán)匡正

審校：任文科

Just days after Beijing’s worst day of air pollution in recent history, IBM is highlighting its efforts to build stronger pollution monitoring and forecasting systems for cities. Big Blue’s China Research Lab has been developing a new system with Beijing’s Environmental Protection Bureau, and on Wednesday announced partnerships with similarly smoggy cities including Johannesburg and Delhi.

According to Dr. Hendrik Hamann, a lead researcher with IBM’s Green Horizons pollution and renewable energy forecasting program, Beijing’s current pollution monitoring methods are both too costly and too imprecise. Only a few dozen monitoring stations are spread out over the huge city, and though they’re highly accurate, there aren’t enough of them to get a detailed picture. IBM IBM 5.04% and BEPB want to supplement those with a network of hundreds or even thousands of inexpensive, networked, low-maintenance sensors about the size of a shoebox.

Data from the sensors would be analyzed, using computer models incorporating weather patterns and even airborne chemical reactions, to produce 72-hour pollution forecasts detailed down to 1km-square areas.

It’s a melding of the Internet of things and big data that epitomizes the movement to smarter cities.

“Stating the obvious,” says Hamann, “What you can’t measure, you can’t manage.”

That’s certainly true of things like traffic congestion and local weather, but it demands the question—what exactly does it mean for a city to ‘manage’ pollution? And how important is it to measure it, compared to just getting rid of it?

On the surface, air quality monitoring can seem merely reactive. Local particulate pollution can spike from one day to the next due to weather patterns, and Hamann even compares a bad pollution day to a hurricane: “I can’t do anything about the hurricane, but I can protect the well-being of citizens.” In Beijing, protecting citizens against pollution means cancelling school for young kids, regulating air intake in big buildings, and government warnings against outdoor activities.

But of course, pollution isn’t quite like a hurricane—humans cause most of it in the first place, and they have some ability to control it, even day to day. Currently, Beijing’s air alert system gives the city broad powers to shut down construction and curtail industrial operations (including its nasty coal-fired power grid), and restrict driving, fireworks, and barbecues.

Those controls have substantial economic costs, and Dr. Hamann says the more granular forecasting IBM has developed would allow them to be more targeted—say, shutting down coal plants in only the parts of a city where it would do the most good.

Even temporarily shutting down coal plants, though, might sound like more short-term mitigation—a way to smooth out the smoggy peaks without changing the underlying problem. But Dr. Hamann believes better data will lead to longer-term improvements through a combination of scenario-based forecasting and public awareness.

Stronger modelling allows more detailed analysis of the costs and benefits of, say, upgrading public transportation to alternative fuels, adding smokestack filters, and increasing energy efficiency. “Look at places like . . . Los Angeles,” Hamann says, which battled its own smog problem for decades. “Through measurement, understanding of the problem, step by step, different actions were taken.”

Hamann thinks better measurement also works from the bottom up, by providing benchmarks that citizens can actually keep track of. That helps synthesize city dwellers’ individual coughs and wheezes into a collective project—in other words, information helps politicize pollution.

But in Beijing, officials haven’t embraced that idea, restricting access to information including the U.S. Embassy’s pollution readings. Though that mindset is improving, China is extremely dependent on coal for power. IBM’s super-accurate data won’t be able to fulfill its promise if those in charge prefer to keep things cloudy.